Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. [https://machinelearningmastery.com/]
SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Human Activity Recognition Using Smartphones dataset is a multi-class classification situation where we are trying to predict one of several (more than two) possible outcomes.
INTRODUCTION: Researchers collected the datasets from experiments that consist of a group of 30 volunteers, with each person performing six activities by wearing a smartphone on the waist. With its embedded accelerometer and gyroscope, the research captured measurement for the activities of WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING. The dataset has been randomly partitioned into two sets, where 70% of the volunteers were selected for generating the training data and 30% the test data.
In previous iterations, the script focused on evaluating various classic machine learning algorithms and identify the algorithm that produces the best accuracy metric. The previous iterations established a baseline performance in terms of accuracy and processing time.
In this Take1 iteration, we will construct and tune an XGBoost machine learning model for this dataset. We will observe the best accuracy result that we can obtain using the XGBoost model with the training and test datasets.
ANALYSIS: For this Take1 iteration, the XGBoost model achieved an accuracy metric of 99.45% in training. When configured with the optimized parameters, the XGBoost model processed the test dataset with an accuracy of 94.94%, which indicated a high variance issue. We will need to explore regularization techniques or other modeling approaches before deploying the model for production use.
CONCLUSION: For this iteration, the XGBoost algorithm achieved the best overall results using the training and test datasets. For this dataset, Random Forest should be considered for further modeling.
Dataset Used: Human Activity Recognition Using Smartphones
Dataset ML Model: Multi-class classification with numerical attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Any predictive modeling machine learning project generally can be broken down into about six major tasks:
!pip install python-dotenv PyMySQL
Collecting python-dotenv
Downloading https://files.pythonhosted.org/packages/cb/2a/07f87440444fdf2c5870a710b6770d766a1c7df9c827b0c90e807f1fb4c5/python_dotenv-0.13.0-py2.py3-none-any.whl
Collecting PyMySQL
Downloading https://files.pythonhosted.org/packages/ed/39/15045ae46f2a123019aa968dfcba0396c161c20f855f11dea6796bcaae95/PyMySQL-0.9.3-py2.py3-none-any.whl (47kB)
|████████████████████████████████| 51kB 2.4MB/s
Installing collected packages: python-dotenv, PyMySQL
Successfully installed PyMySQL-0.9.3 python-dotenv-0.13.0
# Retrieve CPU information from the system
ncpu = !nproc
print("The number of available CPUs is:", ncpu[0])
The number of available CPUs is: 2
# Retrieve GPU configuration information from Colab
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime → "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
Sun Jun 7 20:52:10 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.82 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |
| N/A 35C P0 25W / 250W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
# Retrieve memory configuration information from Colab
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('To enable a high-RAM runtime, select the Runtime → "Change runtime type"')
print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')
print('re-execute this cell.')
else:
print('You are using a high-RAM runtime!')
Your runtime has 13.7 gigabytes of available RAM To enable a high-RAM runtime, select the Runtime → "Change runtime type" menu, and then select High-RAM in the Runtime shape dropdown. Then, re-execute this cell.
# Set the random seed number for reproducible results
seedNum = 888
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
import smtplib
from datetime import datetime
from email.message import EmailMessage
from dotenv import load_dotenv
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier
# Begin the timer for the script processing
startTimeScript = datetime.now()
# Set up the number of CPU cores available for multi-thread processing
# n_jobs = int(ncpu[0])
# Set up the flag to stop sending progress emails (setting to True will send status emails!)
notifyStatus = False
# Set up the parent directory location for loading the dotenv files
useColab = False
if useColab:
# Mount Google Drive locally for storing files
from google.colab import drive
drive.mount('/content/gdrive')
gdrivePrefix = '/content/gdrive/My Drive/Colab_Downloads/'
env_path = '/content/gdrive/My Drive/Colab Notebooks/'
dotenv_path = env_path + "python_script.env"
load_dotenv(dotenv_path=dotenv_path)
# Set up the dotenv file for retrieving environment variables
useLocalPC = False
if useLocalPC:
env_path = "/Users/david/PycharmProjects/"
dotenv_path = env_path + "python_script.env"
load_dotenv(dotenv_path=dotenv_path)
# Configure the plotting style
plt.style.use('seaborn')
# Set Pandas options
pd.set_option("display.max_rows", 120)
pd.set_option("display.width", 140)
# Set the flag for splitting the dataset
splitDataset = False
# splitPercentage = 0.25
# Set the number of folds for cross validation
n_folds = 5
# Set various default modeling parameters
scoring = 'accuracy'
dataset_url = "https://dainesanalytics.com/datasets/ucirvine-human-activity-recognition/"
widthVector = [16] * 561
colNames = ["attr" + str(i).zfill(3) for i in range(1,562)]
X_train_df = pd.read_fwf(dataset_url+'train/X_train.txt', widths=widthVector, header=None, names=colNames)
y_train_df = pd.read_csv(dataset_url+'train/y_train.txt', names=["targetVar"])
Xy_train_df = pd.concat([X_train_df, y_train_df], axis=1)
X_test_df = pd.read_fwf(dataset_url+'test/X_test.txt', widths=widthVector, header=None, names=colNames)
y_test_df = pd.read_csv(dataset_url+'test/y_test.txt', names=["targetVar"])
Xy_test_df = pd.concat([X_test_df, y_test_df], axis=1)
Xy_original = pd.concat([Xy_train_df, Xy_test_df], axis=0)
# Take a peek at the dataframe after import
Xy_original.head(10)
| attr001 | attr002 | attr003 | attr004 | attr005 | attr006 | attr007 | attr008 | attr009 | attr010 | attr011 | attr012 | attr013 | attr014 | attr015 | attr016 | attr017 | attr018 | attr019 | attr020 | attr021 | attr022 | attr023 | attr024 | attr025 | attr026 | attr027 | attr028 | attr029 | attr030 | attr031 | attr032 | attr033 | attr034 | attr035 | attr036 | attr037 | attr038 | attr039 | attr040 | ... | attr523 | attr524 | attr525 | attr526 | attr527 | attr528 | attr529 | attr530 | attr531 | attr532 | attr533 | attr534 | attr535 | attr536 | attr537 | attr538 | attr539 | attr540 | attr541 | attr542 | attr543 | attr544 | attr545 | attr546 | attr547 | attr548 | attr549 | attr550 | attr551 | attr552 | attr553 | attr554 | attr555 | attr556 | attr557 | attr558 | attr559 | attr560 | attr561 | targetVar | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.288585 | -0.020294 | -0.132905 | -0.995279 | -0.983111 | -0.913526 | -0.995112 | -0.983185 | -0.923527 | -0.934724 | -0.567378 | -0.744413 | 0.852947 | 0.685845 | 0.814263 | -0.965523 | -0.999945 | -0.999863 | -0.994612 | -0.994231 | -0.987614 | -0.943220 | -0.407747 | -0.679338 | -0.602122 | 0.929294 | -0.853011 | 0.359910 | -0.058526 | 0.256892 | -0.224848 | 0.264106 | -0.095246 | 0.278851 | -0.465085 | 0.491936 | -0.190884 | 0.376314 | 0.435129 | 0.660790 | ... | -0.991364 | -1.0 | -0.936508 | 0.346989 | -0.516080 | -0.802760 | -0.980135 | -0.961309 | -0.973653 | -0.952264 | -0.989498 | -0.980135 | -0.999240 | -0.992656 | -0.701291 | -1.000000 | -0.128989 | 0.586156 | 0.374605 | -0.991990 | -0.990697 | -0.989941 | -0.992448 | -0.991048 | -0.991990 | -0.999937 | -0.990458 | -0.871306 | -1.000000 | -0.074323 | -0.298676 | -0.710304 | -0.112754 | 0.030400 | -0.464761 | -0.018446 | -0.841247 | 0.179941 | -0.058627 | 5 |
| 1 | 0.278419 | -0.016411 | -0.123520 | -0.998245 | -0.975300 | -0.960322 | -0.998807 | -0.974914 | -0.957686 | -0.943068 | -0.557851 | -0.818409 | 0.849308 | 0.685845 | 0.822637 | -0.981930 | -0.999991 | -0.999788 | -0.998405 | -0.999150 | -0.977866 | -0.948225 | -0.714892 | -0.500930 | -0.570979 | 0.611627 | -0.329549 | 0.284213 | 0.284595 | 0.115705 | -0.090963 | 0.294310 | -0.281211 | 0.085988 | -0.022153 | -0.016657 | -0.220643 | -0.013429 | -0.072692 | 0.579382 | ... | -0.991134 | -1.0 | -0.841270 | 0.532061 | -0.624871 | -0.900160 | -0.988296 | -0.983322 | -0.982659 | -0.986321 | -0.991829 | -0.988296 | -0.999811 | -0.993979 | -0.720683 | -0.948718 | -0.271958 | -0.336310 | -0.720015 | -0.995854 | -0.996399 | -0.995442 | -0.996866 | -0.994440 | -0.995854 | -0.999981 | -0.994544 | -1.000000 | -1.000000 | 0.158075 | -0.595051 | -0.861499 | 0.053477 | -0.007435 | -0.732626 | 0.703511 | -0.844788 | 0.180289 | -0.054317 | 5 |
| 2 | 0.279653 | -0.019467 | -0.113462 | -0.995380 | -0.967187 | -0.978944 | -0.996520 | -0.963668 | -0.977469 | -0.938692 | -0.557851 | -0.818409 | 0.843609 | 0.682401 | 0.839344 | -0.983478 | -0.999969 | -0.999660 | -0.999470 | -0.997130 | -0.964810 | -0.974675 | -0.592235 | -0.485821 | -0.570979 | 0.273025 | -0.086309 | 0.337202 | -0.164739 | 0.017150 | -0.074507 | 0.342256 | -0.332564 | 0.239281 | -0.136204 | 0.173863 | -0.299493 | -0.124698 | -0.181105 | 0.608900 | ... | -0.986658 | -1.0 | -0.904762 | 0.660795 | -0.724697 | -0.928539 | -0.989255 | -0.986028 | -0.984274 | -0.990979 | -0.995703 | -0.989255 | -0.999854 | -0.993238 | -0.736521 | -0.794872 | -0.212728 | -0.535352 | -0.871914 | -0.995031 | -0.995127 | -0.994640 | -0.996060 | -0.995866 | -0.995031 | -0.999973 | -0.993755 | -1.000000 | -0.555556 | 0.414503 | -0.390748 | -0.760104 | -0.118559 | 0.177899 | 0.100699 | 0.808529 | -0.848933 | 0.180637 | -0.049118 | 5 |
| 3 | 0.279174 | -0.026201 | -0.123283 | -0.996091 | -0.983403 | -0.990675 | -0.997099 | -0.982750 | -0.989302 | -0.938692 | -0.576159 | -0.829711 | 0.843609 | 0.682401 | 0.837869 | -0.986093 | -0.999976 | -0.999736 | -0.999504 | -0.997180 | -0.983799 | -0.986007 | -0.627446 | -0.850930 | -0.911872 | 0.061436 | 0.074840 | 0.198204 | -0.264307 | 0.072545 | -0.155320 | 0.323154 | -0.170813 | 0.294938 | -0.306081 | 0.482148 | -0.470129 | -0.305693 | -0.362654 | 0.507459 | ... | -0.988055 | -1.0 | 1.000000 | 0.678921 | -0.701131 | -0.909639 | -0.989413 | -0.987836 | -0.986850 | -0.986749 | -0.996199 | -0.989413 | -0.999876 | -0.989136 | -0.720891 | -1.000000 | -0.035684 | -0.230091 | -0.511217 | -0.995221 | -0.995237 | -0.995722 | -0.995273 | -0.995732 | -0.995221 | -0.999974 | -0.995226 | -0.955696 | -0.936508 | 0.404573 | -0.117290 | -0.482845 | -0.036788 | -0.012892 | 0.640011 | -0.485366 | -0.848649 | 0.181935 | -0.047663 | 5 |
| 4 | 0.276629 | -0.016570 | -0.115362 | -0.998139 | -0.980817 | -0.990482 | -0.998321 | -0.979672 | -0.990441 | -0.942469 | -0.569174 | -0.824705 | 0.849095 | 0.683250 | 0.837869 | -0.992653 | -0.999991 | -0.999856 | -0.999757 | -0.998004 | -0.981232 | -0.991325 | -0.786553 | -0.559477 | -0.761434 | 0.313276 | -0.131208 | 0.191161 | 0.086904 | 0.257615 | -0.272505 | 0.434728 | -0.315375 | 0.439744 | -0.269069 | 0.179414 | -0.088952 | -0.155804 | -0.189763 | 0.599213 | ... | -0.994169 | -1.0 | -1.000000 | 0.559058 | -0.528901 | -0.858933 | -0.991433 | -0.989059 | -0.987744 | -0.991462 | -0.998353 | -0.991433 | -0.999902 | -0.989321 | -0.763372 | -0.897436 | -0.273582 | -0.510282 | -0.830702 | -0.995093 | -0.995465 | -0.995279 | -0.995609 | -0.997418 | -0.995093 | -0.999974 | -0.995487 | -1.000000 | -0.936508 | 0.087753 | -0.351471 | -0.699205 | 0.123320 | 0.122542 | 0.693578 | -0.615971 | -0.847865 | 0.185151 | -0.043892 | 5 |
| 5 | 0.277199 | -0.010098 | -0.105137 | -0.997335 | -0.990487 | -0.995420 | -0.997627 | -0.990218 | -0.995549 | -0.942469 | -0.565684 | -0.822766 | 0.849095 | 0.695586 | 0.845922 | -0.993928 | -0.999985 | -0.999857 | -0.999917 | -0.997584 | -0.991847 | -0.995414 | -0.751869 | -0.454773 | -0.550882 | 0.390052 | -0.182272 | 0.158751 | 0.187313 | 0.259951 | -0.243230 | 0.421736 | -0.418460 | 0.558461 | -0.218344 | 0.165138 | 0.080920 | -0.209979 | -0.151064 | 0.180424 | ... | -0.996697 | -1.0 | -1.000000 | 0.246910 | -0.520879 | -0.802525 | -0.990500 | -0.985861 | -0.985512 | -0.988204 | -0.994758 | -0.990500 | -0.999861 | -0.991967 | -0.768577 | -1.000000 | -0.297329 | -0.346045 | -0.727270 | -0.995143 | -0.995239 | -0.994398 | -0.996088 | -0.998519 | -0.995143 | -0.999974 | -0.994530 | -1.000000 | -1.000000 | 0.019953 | -0.545410 | -0.844619 | 0.082632 | -0.143439 | 0.275041 | -0.368224 | -0.849632 | 0.184823 | -0.042126 | 5 |
| 6 | 0.279454 | -0.019641 | -0.110022 | -0.996921 | -0.967186 | -0.983118 | -0.997003 | -0.966097 | -0.983116 | -0.940987 | -0.565684 | -0.817189 | 0.851040 | 0.674347 | 0.833591 | -0.986837 | -0.999982 | -0.999658 | -0.999634 | -0.996863 | -0.972402 | -0.982864 | -0.637185 | -0.514751 | -0.536553 | 0.360460 | -0.233335 | 0.226457 | 0.069525 | 0.064297 | -0.076439 | 0.138057 | -0.036769 | 0.231401 | -0.114577 | 0.319204 | -0.487769 | -0.095852 | -0.135729 | 0.610845 | ... | -0.991345 | -1.0 | -0.904762 | 0.290177 | -0.668835 | -0.933443 | -0.988269 | -0.984569 | -0.983601 | -0.985328 | -0.994521 | -0.988269 | -0.999828 | -0.989040 | -0.735639 | -1.000000 | -0.257032 | -0.321591 | -0.658435 | -0.995641 | -0.994639 | -0.994202 | -0.994491 | -0.996934 | -0.995641 | -0.999974 | -0.993940 | -0.955696 | -1.000000 | 0.145844 | -0.217198 | -0.564430 | -0.212754 | -0.230622 | 0.014637 | -0.189512 | -0.852150 | 0.182170 | -0.043010 | 5 |
| 7 | 0.277432 | -0.030488 | -0.125360 | -0.996559 | -0.966728 | -0.981585 | -0.996485 | -0.966313 | -0.982982 | -0.940987 | -0.572638 | -0.817189 | 0.850328 | 0.670410 | 0.832383 | -0.976851 | -0.999980 | -0.999341 | -0.999164 | -0.995768 | -0.975847 | -0.986829 | -0.633239 | -0.743877 | -0.822408 | 0.363064 | -0.300719 | 0.346867 | -0.063694 | 0.144124 | -0.132349 | 0.083232 | 0.041631 | 0.137192 | -0.092290 | 0.372369 | -0.720913 | -0.161634 | -0.017120 | 0.561421 | ... | -0.989676 | -1.0 | -0.968254 | 0.249799 | -0.655503 | -0.932563 | -0.989431 | -0.987065 | -0.985978 | -0.988193 | -0.998240 | -0.989431 | -0.999867 | -0.990035 | -0.735639 | -1.000000 | -0.197272 | -0.407284 | -0.733313 | -0.995629 | -0.994507 | -0.994727 | -0.994018 | -0.997006 | -0.995629 | -0.999974 | -0.995423 | -0.955696 | -1.000000 | 0.136382 | -0.082307 | -0.421715 | -0.020888 | 0.593996 | -0.561871 | 0.467383 | -0.851017 | 0.183779 | -0.041976 | 5 |
| 8 | 0.277293 | -0.021751 | -0.120751 | -0.997328 | -0.961245 | -0.983672 | -0.997596 | -0.957236 | -0.984379 | -0.940598 | -0.564175 | -0.823527 | 0.850328 | 0.670410 | 0.832383 | -0.983295 | -0.999985 | -0.999528 | -0.999426 | -0.997351 | -0.957212 | -0.985841 | -0.683117 | -0.524812 | -0.758524 | 0.358268 | -0.253309 | 0.266929 | -0.124250 | 0.215324 | -0.183362 | 0.171745 | -0.068957 | 0.241831 | -0.194419 | 0.216758 | -0.338026 | -0.164791 | -0.033767 | 0.724234 | ... | -0.992371 | -1.0 | -1.000000 | 0.272342 | -0.750485 | -0.929474 | -0.991629 | -0.991759 | -0.991182 | -0.991522 | -0.995197 | -0.991629 | -0.999927 | -0.991428 | -0.850310 | -1.000000 | 0.073417 | -0.371389 | -0.674770 | -0.995260 | -0.996007 | -0.995828 | -0.995482 | -0.993608 | -0.995260 | -0.999977 | -0.995095 | -0.955696 | -1.000000 | 0.314038 | -0.269401 | -0.572995 | 0.012954 | 0.080936 | -0.234313 | 0.117797 | -0.847971 | 0.188982 | -0.037364 | 5 |
| 9 | 0.280586 | -0.009960 | -0.106065 | -0.994803 | -0.972758 | -0.986244 | -0.995405 | -0.973663 | -0.985642 | -0.940028 | -0.554594 | -0.815850 | 0.845442 | 0.684757 | 0.838455 | -0.986541 | -0.999963 | -0.999662 | -0.999718 | -0.996163 | -0.979809 | -0.982811 | -0.550721 | -0.294553 | -0.479711 | 0.192863 | -0.021212 | 0.037915 | 0.190540 | 0.039722 | 0.054283 | 0.037268 | 0.081551 | 0.172340 | 0.035995 | -0.184930 | 0.351156 | -0.176241 | -0.447651 | 0.391867 | ... | -0.979144 | -1.0 | -1.000000 | 0.250884 | -0.510116 | -0.801919 | -0.984788 | -0.980388 | -0.981850 | -0.976771 | -0.993197 | -0.984788 | -0.999736 | -0.981969 | -0.671366 | -1.000000 | -0.139966 | 0.106785 | -0.131360 | -0.990981 | -0.990599 | -0.991665 | -0.988199 | -0.984877 | -0.990981 | -0.999929 | -0.990456 | -0.955696 | -1.000000 | 0.267383 | 0.339526 | 0.140452 | -0.020590 | -0.127730 | -0.482871 | -0.070670 | -0.848294 | 0.190310 | -0.034417 | 5 |
10 rows × 562 columns
Xy_original.info(verbose=True)
<class 'pandas.core.frame.DataFrame'> Int64Index: 10299 entries, 0 to 2946 Data columns (total 562 columns): # Column Dtype --- ------ ----- 0 attr001 float64 1 attr002 float64 2 attr003 float64 3 attr004 float64 4 attr005 float64 5 attr006 float64 6 attr007 float64 7 attr008 float64 8 attr009 float64 9 attr010 float64 10 attr011 float64 11 attr012 float64 12 attr013 float64 13 attr014 float64 14 attr015 float64 15 attr016 float64 16 attr017 float64 17 attr018 float64 18 attr019 float64 19 attr020 float64 20 attr021 float64 21 attr022 float64 22 attr023 float64 23 attr024 float64 24 attr025 float64 25 attr026 float64 26 attr027 float64 27 attr028 float64 28 attr029 float64 29 attr030 float64 30 attr031 float64 31 attr032 float64 32 attr033 float64 33 attr034 float64 34 attr035 float64 35 attr036 float64 36 attr037 float64 37 attr038 float64 38 attr039 float64 39 attr040 float64 40 attr041 float64 41 attr042 float64 42 attr043 float64 43 attr044 float64 44 attr045 float64 45 attr046 float64 46 attr047 float64 47 attr048 float64 48 attr049 float64 49 attr050 float64 50 attr051 float64 51 attr052 float64 52 attr053 float64 53 attr054 float64 54 attr055 float64 55 attr056 float64 56 attr057 float64 57 attr058 float64 58 attr059 float64 59 attr060 float64 60 attr061 float64 61 attr062 float64 62 attr063 float64 63 attr064 float64 64 attr065 float64 65 attr066 float64 66 attr067 float64 67 attr068 float64 68 attr069 float64 69 attr070 float64 70 attr071 float64 71 attr072 float64 72 attr073 float64 73 attr074 float64 74 attr075 float64 75 attr076 float64 76 attr077 float64 77 attr078 float64 78 attr079 float64 79 attr080 float64 80 attr081 float64 81 attr082 float64 82 attr083 float64 83 attr084 float64 84 attr085 float64 85 attr086 float64 86 attr087 float64 87 attr088 float64 88 attr089 float64 89 attr090 float64 90 attr091 float64 91 attr092 float64 92 attr093 float64 93 attr094 float64 94 attr095 float64 95 attr096 float64 96 attr097 float64 97 attr098 float64 98 attr099 float64 99 attr100 float64 100 attr101 float64 101 attr102 float64 102 attr103 float64 103 attr104 float64 104 attr105 float64 105 attr106 float64 106 attr107 float64 107 attr108 float64 108 attr109 float64 109 attr110 float64 110 attr111 float64 111 attr112 float64 112 attr113 float64 113 attr114 float64 114 attr115 float64 115 attr116 float64 116 attr117 float64 117 attr118 float64 118 attr119 float64 119 attr120 float64 120 attr121 float64 121 attr122 float64 122 attr123 float64 123 attr124 float64 124 attr125 float64 125 attr126 float64 126 attr127 float64 127 attr128 float64 128 attr129 float64 129 attr130 float64 130 attr131 float64 131 attr132 float64 132 attr133 float64 133 attr134 float64 134 attr135 float64 135 attr136 float64 136 attr137 float64 137 attr138 float64 138 attr139 float64 139 attr140 float64 140 attr141 float64 141 attr142 float64 142 attr143 float64 143 attr144 float64 144 attr145 float64 145 attr146 float64 146 attr147 float64 147 attr148 float64 148 attr149 float64 149 attr150 float64 150 attr151 float64 151 attr152 float64 152 attr153 float64 153 attr154 float64 154 attr155 float64 155 attr156 float64 156 attr157 float64 157 attr158 float64 158 attr159 float64 159 attr160 float64 160 attr161 float64 161 attr162 float64 162 attr163 float64 163 attr164 float64 164 attr165 float64 165 attr166 float64 166 attr167 float64 167 attr168 float64 168 attr169 float64 169 attr170 float64 170 attr171 float64 171 attr172 float64 172 attr173 float64 173 attr174 float64 174 attr175 float64 175 attr176 float64 176 attr177 float64 177 attr178 float64 178 attr179 float64 179 attr180 float64 180 attr181 float64 181 attr182 float64 182 attr183 float64 183 attr184 float64 184 attr185 float64 185 attr186 float64 186 attr187 float64 187 attr188 float64 188 attr189 float64 189 attr190 float64 190 attr191 float64 191 attr192 float64 192 attr193 float64 193 attr194 float64 194 attr195 float64 195 attr196 float64 196 attr197 float64 197 attr198 float64 198 attr199 float64 199 attr200 float64 200 attr201 float64 201 attr202 float64 202 attr203 float64 203 attr204 float64 204 attr205 float64 205 attr206 float64 206 attr207 float64 207 attr208 float64 208 attr209 float64 209 attr210 float64 210 attr211 float64 211 attr212 float64 212 attr213 float64 213 attr214 float64 214 attr215 float64 215 attr216 float64 216 attr217 float64 217 attr218 float64 218 attr219 float64 219 attr220 float64 220 attr221 float64 221 attr222 float64 222 attr223 float64 223 attr224 float64 224 attr225 float64 225 attr226 float64 226 attr227 float64 227 attr228 float64 228 attr229 float64 229 attr230 float64 230 attr231 float64 231 attr232 float64 232 attr233 float64 233 attr234 float64 234 attr235 float64 235 attr236 float64 236 attr237 float64 237 attr238 float64 238 attr239 float64 239 attr240 float64 240 attr241 float64 241 attr242 float64 242 attr243 float64 243 attr244 float64 244 attr245 float64 245 attr246 float64 246 attr247 float64 247 attr248 float64 248 attr249 float64 249 attr250 float64 250 attr251 float64 251 attr252 float64 252 attr253 float64 253 attr254 float64 254 attr255 float64 255 attr256 float64 256 attr257 float64 257 attr258 float64 258 attr259 float64 259 attr260 float64 260 attr261 float64 261 attr262 float64 262 attr263 float64 263 attr264 float64 264 attr265 float64 265 attr266 float64 266 attr267 float64 267 attr268 float64 268 attr269 float64 269 attr270 float64 270 attr271 float64 271 attr272 float64 272 attr273 float64 273 attr274 float64 274 attr275 float64 275 attr276 float64 276 attr277 float64 277 attr278 float64 278 attr279 float64 279 attr280 float64 280 attr281 float64 281 attr282 float64 282 attr283 float64 283 attr284 float64 284 attr285 float64 285 attr286 float64 286 attr287 float64 287 attr288 float64 288 attr289 float64 289 attr290 float64 290 attr291 float64 291 attr292 float64 292 attr293 float64 293 attr294 float64 294 attr295 float64 295 attr296 float64 296 attr297 float64 297 attr298 float64 298 attr299 float64 299 attr300 float64 300 attr301 float64 301 attr302 float64 302 attr303 float64 303 attr304 float64 304 attr305 float64 305 attr306 float64 306 attr307 float64 307 attr308 float64 308 attr309 float64 309 attr310 float64 310 attr311 float64 311 attr312 float64 312 attr313 float64 313 attr314 float64 314 attr315 float64 315 attr316 float64 316 attr317 float64 317 attr318 float64 318 attr319 float64 319 attr320 float64 320 attr321 float64 321 attr322 float64 322 attr323 float64 323 attr324 float64 324 attr325 float64 325 attr326 float64 326 attr327 float64 327 attr328 float64 328 attr329 float64 329 attr330 float64 330 attr331 float64 331 attr332 float64 332 attr333 float64 333 attr334 float64 334 attr335 float64 335 attr336 float64 336 attr337 float64 337 attr338 float64 338 attr339 float64 339 attr340 float64 340 attr341 float64 341 attr342 float64 342 attr343 float64 343 attr344 float64 344 attr345 float64 345 attr346 float64 346 attr347 float64 347 attr348 float64 348 attr349 float64 349 attr350 float64 350 attr351 float64 351 attr352 float64 352 attr353 float64 353 attr354 float64 354 attr355 float64 355 attr356 float64 356 attr357 float64 357 attr358 float64 358 attr359 float64 359 attr360 float64 360 attr361 float64 361 attr362 float64 362 attr363 float64 363 attr364 float64 364 attr365 float64 365 attr366 float64 366 attr367 float64 367 attr368 float64 368 attr369 float64 369 attr370 float64 370 attr371 float64 371 attr372 float64 372 attr373 float64 373 attr374 float64 374 attr375 float64 375 attr376 float64 376 attr377 float64 377 attr378 float64 378 attr379 float64 379 attr380 float64 380 attr381 float64 381 attr382 float64 382 attr383 float64 383 attr384 float64 384 attr385 float64 385 attr386 float64 386 attr387 float64 387 attr388 float64 388 attr389 float64 389 attr390 float64 390 attr391 float64 391 attr392 float64 392 attr393 float64 393 attr394 float64 394 attr395 float64 395 attr396 float64 396 attr397 float64 397 attr398 float64 398 attr399 float64 399 attr400 float64 400 attr401 float64 401 attr402 float64 402 attr403 float64 403 attr404 float64 404 attr405 float64 405 attr406 float64 406 attr407 float64 407 attr408 float64 408 attr409 float64 409 attr410 float64 410 attr411 float64 411 attr412 float64 412 attr413 float64 413 attr414 float64 414 attr415 float64 415 attr416 float64 416 attr417 float64 417 attr418 float64 418 attr419 float64 419 attr420 float64 420 attr421 float64 421 attr422 float64 422 attr423 float64 423 attr424 float64 424 attr425 float64 425 attr426 float64 426 attr427 float64 427 attr428 float64 428 attr429 float64 429 attr430 float64 430 attr431 float64 431 attr432 float64 432 attr433 float64 433 attr434 float64 434 attr435 float64 435 attr436 float64 436 attr437 float64 437 attr438 float64 438 attr439 float64 439 attr440 float64 440 attr441 float64 441 attr442 float64 442 attr443 float64 443 attr444 float64 444 attr445 float64 445 attr446 float64 446 attr447 float64 447 attr448 float64 448 attr449 float64 449 attr450 float64 450 attr451 float64 451 attr452 float64 452 attr453 float64 453 attr454 float64 454 attr455 float64 455 attr456 float64 456 attr457 float64 457 attr458 float64 458 attr459 float64 459 attr460 float64 460 attr461 float64 461 attr462 float64 462 attr463 float64 463 attr464 float64 464 attr465 float64 465 attr466 float64 466 attr467 float64 467 attr468 float64 468 attr469 float64 469 attr470 float64 470 attr471 float64 471 attr472 float64 472 attr473 float64 473 attr474 float64 474 attr475 float64 475 attr476 float64 476 attr477 float64 477 attr478 float64 478 attr479 float64 479 attr480 float64 480 attr481 float64 481 attr482 float64 482 attr483 float64 483 attr484 float64 484 attr485 float64 485 attr486 float64 486 attr487 float64 487 attr488 float64 488 attr489 float64 489 attr490 float64 490 attr491 float64 491 attr492 float64 492 attr493 float64 493 attr494 float64 494 attr495 float64 495 attr496 float64 496 attr497 float64 497 attr498 float64 498 attr499 float64 499 attr500 float64 500 attr501 float64 501 attr502 float64 502 attr503 float64 503 attr504 float64 504 attr505 float64 505 attr506 float64 506 attr507 float64 507 attr508 float64 508 attr509 float64 509 attr510 float64 510 attr511 float64 511 attr512 float64 512 attr513 float64 513 attr514 float64 514 attr515 float64 515 attr516 float64 516 attr517 float64 517 attr518 float64 518 attr519 float64 519 attr520 float64 520 attr521 float64 521 attr522 float64 522 attr523 float64 523 attr524 float64 524 attr525 float64 525 attr526 float64 526 attr527 float64 527 attr528 float64 528 attr529 float64 529 attr530 float64 530 attr531 float64 531 attr532 float64 532 attr533 float64 533 attr534 float64 534 attr535 float64 535 attr536 float64 536 attr537 float64 537 attr538 float64 538 attr539 float64 539 attr540 float64 540 attr541 float64 541 attr542 float64 542 attr543 float64 543 attr544 float64 544 attr545 float64 545 attr546 float64 546 attr547 float64 547 attr548 float64 548 attr549 float64 549 attr550 float64 550 attr551 float64 551 attr552 float64 552 attr553 float64 553 attr554 float64 554 attr555 float64 555 attr556 float64 556 attr557 float64 557 attr558 float64 558 attr559 float64 559 attr560 float64 560 attr561 float64 561 targetVar int64 dtypes: float64(561), int64(1) memory usage: 44.2 MB
Xy_original.describe()
| attr001 | attr002 | attr003 | attr004 | attr005 | attr006 | attr007 | attr008 | attr009 | attr010 | attr011 | attr012 | attr013 | attr014 | attr015 | attr016 | attr017 | attr018 | attr019 | attr020 | attr021 | attr022 | attr023 | attr024 | attr025 | attr026 | attr027 | attr028 | attr029 | attr030 | attr031 | attr032 | attr033 | attr034 | attr035 | attr036 | attr037 | attr038 | attr039 | attr040 | ... | attr523 | attr524 | attr525 | attr526 | attr527 | attr528 | attr529 | attr530 | attr531 | attr532 | attr533 | attr534 | attr535 | attr536 | attr537 | attr538 | attr539 | attr540 | attr541 | attr542 | attr543 | attr544 | attr545 | attr546 | attr547 | attr548 | attr549 | attr550 | attr551 | attr552 | attr553 | attr554 | attr555 | attr556 | attr557 | attr558 | attr559 | attr560 | attr561 | targetVar | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | ... | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 | 10299.000000 |
| mean | 0.274347 | -0.017743 | -0.108925 | -0.607784 | -0.510191 | -0.613064 | -0.633593 | -0.525697 | -0.614989 | -0.466732 | -0.305180 | -0.562230 | 0.525304 | 0.389537 | 0.598022 | -0.552087 | -0.825460 | -0.902704 | -0.854662 | -0.689162 | -0.643512 | -0.640686 | -0.100332 | -0.128765 | -0.157863 | -0.118954 | 0.108574 | -0.035699 | 0.122000 | -0.029677 | 0.031724 | 0.155148 | -0.018077 | 0.006110 | 0.037729 | 0.034424 | -0.082669 | -0.120309 | -0.197746 | 0.102199 | ... | -0.676629 | -0.338469 | -0.877800 | 0.173220 | -0.298598 | -0.601659 | -0.697411 | -0.699976 | -0.681014 | -0.734623 | -0.888701 | -0.697411 | -0.881301 | -0.722125 | -0.076279 | -0.886999 | -0.041564 | -0.264279 | -0.575866 | -0.779768 | -0.792190 | -0.773404 | -0.809934 | -0.871201 | -0.779768 | -0.937898 | -0.772715 | -0.274339 | -0.900033 | 0.126708 | -0.298592 | -0.617700 | 0.007705 | 0.002648 | 0.017683 | -0.009219 | -0.496522 | 0.063255 | -0.054284 | 3.624624 |
| std | 0.067628 | 0.037128 | 0.053033 | 0.438694 | 0.500240 | 0.403657 | 0.413333 | 0.484201 | 0.399034 | 0.538707 | 0.279920 | 0.282991 | 0.356589 | 0.338844 | 0.290615 | 0.461375 | 0.247052 | 0.125988 | 0.205998 | 0.359209 | 0.368865 | 0.372065 | 0.462315 | 0.433687 | 0.368102 | 0.307721 | 0.247140 | 0.247820 | 0.232044 | 0.254461 | 0.213708 | 0.208718 | 0.220257 | 0.281722 | 0.215456 | 0.236854 | 0.230957 | 0.357677 | 0.325103 | 0.376049 | ... | 0.365550 | 0.666857 | 0.189655 | 0.252537 | 0.364723 | 0.353928 | 0.323701 | 0.310443 | 0.331222 | 0.281107 | 0.163587 | 0.323701 | 0.180432 | 0.310562 | 0.602930 | 0.159981 | 0.280142 | 0.322579 | 0.320006 | 0.267592 | 0.259160 | 0.279727 | 0.242697 | 0.189627 | 0.267592 | 0.128284 | 0.278403 | 0.624272 | 0.139691 | 0.245443 | 0.320199 | 0.308796 | 0.336591 | 0.447364 | 0.616188 | 0.484770 | 0.511158 | 0.305468 | 0.268898 | 1.743695 |
| min | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | ... | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | 1.000000 |
| 25% | 0.262625 | -0.024902 | -0.121019 | -0.992360 | -0.976990 | -0.979137 | -0.993293 | -0.977017 | -0.979064 | -0.935788 | -0.562570 | -0.812194 | 0.212530 | 0.113900 | 0.392717 | -0.981706 | -0.999929 | -0.999771 | -0.999414 | -0.994185 | -0.981327 | -0.978488 | -0.563834 | -0.549611 | -0.496825 | -0.368555 | -0.079017 | -0.189949 | -0.033860 | -0.221971 | -0.129050 | 0.028955 | -0.165678 | -0.206485 | -0.118064 | -0.110770 | -0.239535 | -0.361669 | -0.408798 | -0.140909 | ... | -0.988546 | -1.000000 | -0.968254 | -0.002959 | -0.601439 | -0.878842 | -0.982502 | -0.978150 | -0.978668 | -0.979648 | -0.993815 | -0.982502 | -0.999668 | -0.985373 | -0.665001 | -1.000000 | -0.234356 | -0.499586 | -0.807658 | -0.992108 | -0.992575 | -0.991675 | -0.993546 | -0.993657 | -0.992108 | -0.999946 | -0.991164 | -0.923452 | -0.968254 | -0.019481 | -0.536174 | -0.841847 | -0.124694 | -0.287031 | -0.493108 | -0.389041 | -0.817288 | 0.002151 | -0.131880 | 2.000000 |
| 50% | 0.277174 | -0.017162 | -0.108596 | -0.943030 | -0.835032 | -0.850773 | -0.948244 | -0.843670 | -0.845068 | -0.874825 | -0.468206 | -0.724503 | 0.784233 | 0.619774 | 0.772226 | -0.876947 | -0.997736 | -0.992909 | -0.984240 | -0.955999 | -0.884947 | -0.853776 | -0.057117 | -0.101741 | -0.136380 | -0.136206 | 0.077529 | -0.017644 | 0.126279 | -0.045486 | 0.017654 | 0.160661 | -0.018927 | 0.020698 | 0.009945 | 0.045355 | -0.083301 | -0.161167 | -0.191758 | 0.135572 | ... | -0.942217 | -0.682105 | -0.904762 | 0.164180 | -0.347522 | -0.713718 | -0.875620 | -0.827490 | -0.845626 | -0.827234 | -0.959151 | -0.875620 | -0.984283 | -0.912569 | -0.155022 | -0.948718 | -0.052095 | -0.317706 | -0.664937 | -0.945344 | -0.938212 | -0.935101 | -0.943402 | -0.972651 | -0.945344 | -0.998043 | -0.941888 | -0.414503 | -0.904762 | 0.136245 | -0.335160 | -0.703402 | 0.008146 | 0.007668 | 0.017192 | -0.007186 | -0.715631 | 0.182028 | -0.003882 | 4.000000 |
| 75% | 0.288354 | -0.010625 | -0.097589 | -0.250293 | -0.057336 | -0.278737 | -0.302033 | -0.087405 | -0.288149 | -0.014641 | -0.067345 | -0.345591 | 0.843793 | 0.685194 | 0.836742 | -0.122829 | -0.715745 | -0.825149 | -0.759473 | -0.407902 | -0.324653 | -0.336393 | 0.329587 | 0.283141 | 0.167352 | 0.133245 | 0.286068 | 0.133317 | 0.277676 | 0.163282 | 0.180850 | 0.288163 | 0.131190 | 0.223530 | 0.179591 | 0.194290 | 0.074748 | 0.080148 | 0.002491 | 0.372200 | ... | -0.371697 | 0.346184 | -0.873016 | 0.357307 | -0.057690 | -0.425813 | -0.451400 | -0.471267 | -0.418532 | -0.555974 | -0.839875 | -0.451400 | -0.814917 | -0.495328 | 0.513909 | -0.846154 | 0.151575 | -0.084974 | -0.439280 | -0.612242 | -0.643738 | -0.609846 | -0.684886 | -0.805758 | -0.612242 | -0.922682 | -0.604730 | 0.337220 | -0.873016 | 0.288960 | -0.113167 | -0.487981 | 0.149005 | 0.291490 | 0.536137 | 0.365996 | -0.521503 | 0.250790 | 0.102970 | 5.000000 |
| max | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 6.000000 |
8 rows × 562 columns
Xy_original.isnull().sum()
attr001 0
attr002 0
attr003 0
attr004 0
attr005 0
..
attr558 0
attr559 0
attr560 0
attr561 0
targetVar 0
Length: 562, dtype: int64
print('Total number of NaN in the dataframe: ', Xy_original.isnull().sum().sum())
Total number of NaN in the dataframe: 0
# Not applicable for this iteration of the project
# Use variable totCol to hold the number of columns in the dataframe
totCol = len(Xy_original.columns)
# Set up variable totAttr for the total number of attribute columns
totAttr = totCol-1
# targetCol variable indicates the column location of the target/class variable
# If the first column, set targetCol to 1. If the last column, set targetCol to totCol
# If (targetCol <> 1) and (targetCol <> totCol), be aware when slicing up the dataframes for visualization
targetCol = totCol
# Set up the number of row and columns for visualization display. dispRow * dispCol should be >= totAttr
dispCol = 4
if totAttr % dispCol == 0 :
dispRow = totAttr // dispCol
else :
dispRow = (totAttr // dispCol) + 1
# Set figure width to display the data visualization plots
fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = dispCol*4
fig_size[1] = dispRow*4
plt.rcParams["figure.figsize"] = fig_size
X_train_df.head(10)
| attr001 | attr002 | attr003 | attr004 | attr005 | attr006 | attr007 | attr008 | attr009 | attr010 | attr011 | attr012 | attr013 | attr014 | attr015 | attr016 | attr017 | attr018 | attr019 | attr020 | attr021 | attr022 | attr023 | attr024 | attr025 | attr026 | attr027 | attr028 | attr029 | attr030 | attr031 | attr032 | attr033 | attr034 | attr035 | attr036 | attr037 | attr038 | attr039 | attr040 | ... | attr522 | attr523 | attr524 | attr525 | attr526 | attr527 | attr528 | attr529 | attr530 | attr531 | attr532 | attr533 | attr534 | attr535 | attr536 | attr537 | attr538 | attr539 | attr540 | attr541 | attr542 | attr543 | attr544 | attr545 | attr546 | attr547 | attr548 | attr549 | attr550 | attr551 | attr552 | attr553 | attr554 | attr555 | attr556 | attr557 | attr558 | attr559 | attr560 | attr561 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.288585 | -0.020294 | -0.132905 | -0.995279 | -0.983111 | -0.913526 | -0.995112 | -0.983185 | -0.923527 | -0.934724 | -0.567378 | -0.744413 | 0.852947 | 0.685845 | 0.814263 | -0.965523 | -0.999945 | -0.999863 | -0.994612 | -0.994231 | -0.987614 | -0.943220 | -0.407747 | -0.679338 | -0.602122 | 0.929294 | -0.853011 | 0.359910 | -0.058526 | 0.256892 | -0.224848 | 0.264106 | -0.095246 | 0.278851 | -0.465085 | 0.491936 | -0.190884 | 0.376314 | 0.435129 | 0.660790 | ... | -0.999918 | -0.991364 | -1.0 | -0.936508 | 0.346989 | -0.516080 | -0.802760 | -0.980135 | -0.961309 | -0.973653 | -0.952264 | -0.989498 | -0.980135 | -0.999240 | -0.992656 | -0.701291 | -1.000000 | -0.128989 | 0.586156 | 0.374605 | -0.991990 | -0.990697 | -0.989941 | -0.992448 | -0.991048 | -0.991990 | -0.999937 | -0.990458 | -0.871306 | -1.000000 | -0.074323 | -0.298676 | -0.710304 | -0.112754 | 0.030400 | -0.464761 | -0.018446 | -0.841247 | 0.179941 | -0.058627 |
| 1 | 0.278419 | -0.016411 | -0.123520 | -0.998245 | -0.975300 | -0.960322 | -0.998807 | -0.974914 | -0.957686 | -0.943068 | -0.557851 | -0.818409 | 0.849308 | 0.685845 | 0.822637 | -0.981930 | -0.999991 | -0.999788 | -0.998405 | -0.999150 | -0.977866 | -0.948225 | -0.714892 | -0.500930 | -0.570979 | 0.611627 | -0.329549 | 0.284213 | 0.284595 | 0.115705 | -0.090963 | 0.294310 | -0.281211 | 0.085988 | -0.022153 | -0.016657 | -0.220643 | -0.013429 | -0.072692 | 0.579382 | ... | -0.999867 | -0.991134 | -1.0 | -0.841270 | 0.532061 | -0.624871 | -0.900160 | -0.988296 | -0.983322 | -0.982659 | -0.986321 | -0.991829 | -0.988296 | -0.999811 | -0.993979 | -0.720683 | -0.948718 | -0.271958 | -0.336310 | -0.720015 | -0.995854 | -0.996399 | -0.995442 | -0.996866 | -0.994440 | -0.995854 | -0.999981 | -0.994544 | -1.000000 | -1.000000 | 0.158075 | -0.595051 | -0.861499 | 0.053477 | -0.007435 | -0.732626 | 0.703511 | -0.844788 | 0.180289 | -0.054317 |
| 2 | 0.279653 | -0.019467 | -0.113462 | -0.995380 | -0.967187 | -0.978944 | -0.996520 | -0.963668 | -0.977469 | -0.938692 | -0.557851 | -0.818409 | 0.843609 | 0.682401 | 0.839344 | -0.983478 | -0.999969 | -0.999660 | -0.999470 | -0.997130 | -0.964810 | -0.974675 | -0.592235 | -0.485821 | -0.570979 | 0.273025 | -0.086309 | 0.337202 | -0.164739 | 0.017150 | -0.074507 | 0.342256 | -0.332564 | 0.239281 | -0.136204 | 0.173863 | -0.299493 | -0.124698 | -0.181105 | 0.608900 | ... | -0.999845 | -0.986658 | -1.0 | -0.904762 | 0.660795 | -0.724697 | -0.928539 | -0.989255 | -0.986028 | -0.984274 | -0.990979 | -0.995703 | -0.989255 | -0.999854 | -0.993238 | -0.736521 | -0.794872 | -0.212728 | -0.535352 | -0.871914 | -0.995031 | -0.995127 | -0.994640 | -0.996060 | -0.995866 | -0.995031 | -0.999973 | -0.993755 | -1.000000 | -0.555556 | 0.414503 | -0.390748 | -0.760104 | -0.118559 | 0.177899 | 0.100699 | 0.808529 | -0.848933 | 0.180637 | -0.049118 |
| 3 | 0.279174 | -0.026201 | -0.123283 | -0.996091 | -0.983403 | -0.990675 | -0.997099 | -0.982750 | -0.989302 | -0.938692 | -0.576159 | -0.829711 | 0.843609 | 0.682401 | 0.837869 | -0.986093 | -0.999976 | -0.999736 | -0.999504 | -0.997180 | -0.983799 | -0.986007 | -0.627446 | -0.850930 | -0.911872 | 0.061436 | 0.074840 | 0.198204 | -0.264307 | 0.072545 | -0.155320 | 0.323154 | -0.170813 | 0.294938 | -0.306081 | 0.482148 | -0.470129 | -0.305693 | -0.362654 | 0.507459 | ... | -0.999895 | -0.988055 | -1.0 | 1.000000 | 0.678921 | -0.701131 | -0.909639 | -0.989413 | -0.987836 | -0.986850 | -0.986749 | -0.996199 | -0.989413 | -0.999876 | -0.989136 | -0.720891 | -1.000000 | -0.035684 | -0.230091 | -0.511217 | -0.995221 | -0.995237 | -0.995722 | -0.995273 | -0.995732 | -0.995221 | -0.999974 | -0.995226 | -0.955696 | -0.936508 | 0.404573 | -0.117290 | -0.482845 | -0.036788 | -0.012892 | 0.640011 | -0.485366 | -0.848649 | 0.181935 | -0.047663 |
| 4 | 0.276629 | -0.016570 | -0.115362 | -0.998139 | -0.980817 | -0.990482 | -0.998321 | -0.979672 | -0.990441 | -0.942469 | -0.569174 | -0.824705 | 0.849095 | 0.683250 | 0.837869 | -0.992653 | -0.999991 | -0.999856 | -0.999757 | -0.998004 | -0.981232 | -0.991325 | -0.786553 | -0.559477 | -0.761434 | 0.313276 | -0.131208 | 0.191161 | 0.086904 | 0.257615 | -0.272505 | 0.434728 | -0.315375 | 0.439744 | -0.269069 | 0.179414 | -0.088952 | -0.155804 | -0.189763 | 0.599213 | ... | -0.999941 | -0.994169 | -1.0 | -1.000000 | 0.559058 | -0.528901 | -0.858933 | -0.991433 | -0.989059 | -0.987744 | -0.991462 | -0.998353 | -0.991433 | -0.999902 | -0.989321 | -0.763372 | -0.897436 | -0.273582 | -0.510282 | -0.830702 | -0.995093 | -0.995465 | -0.995279 | -0.995609 | -0.997418 | -0.995093 | -0.999974 | -0.995487 | -1.000000 | -0.936508 | 0.087753 | -0.351471 | -0.699205 | 0.123320 | 0.122542 | 0.693578 | -0.615971 | -0.847865 | 0.185151 | -0.043892 |
| 5 | 0.277199 | -0.010098 | -0.105137 | -0.997335 | -0.990487 | -0.995420 | -0.997627 | -0.990218 | -0.995549 | -0.942469 | -0.565684 | -0.822766 | 0.849095 | 0.695586 | 0.845922 | -0.993928 | -0.999985 | -0.999857 | -0.999917 | -0.997584 | -0.991847 | -0.995414 | -0.751869 | -0.454773 | -0.550882 | 0.390052 | -0.182272 | 0.158751 | 0.187313 | 0.259951 | -0.243230 | 0.421736 | -0.418460 | 0.558461 | -0.218344 | 0.165138 | 0.080920 | -0.209979 | -0.151064 | 0.180424 | ... | -0.999937 | -0.996697 | -1.0 | -1.000000 | 0.246910 | -0.520879 | -0.802525 | -0.990500 | -0.985861 | -0.985512 | -0.988204 | -0.994758 | -0.990500 | -0.999861 | -0.991967 | -0.768577 | -1.000000 | -0.297329 | -0.346045 | -0.727270 | -0.995143 | -0.995239 | -0.994398 | -0.996088 | -0.998519 | -0.995143 | -0.999974 | -0.994530 | -1.000000 | -1.000000 | 0.019953 | -0.545410 | -0.844619 | 0.082632 | -0.143439 | 0.275041 | -0.368224 | -0.849632 | 0.184823 | -0.042126 |
| 6 | 0.279454 | -0.019641 | -0.110022 | -0.996921 | -0.967186 | -0.983118 | -0.997003 | -0.966097 | -0.983116 | -0.940987 | -0.565684 | -0.817189 | 0.851040 | 0.674347 | 0.833591 | -0.986837 | -0.999982 | -0.999658 | -0.999634 | -0.996863 | -0.972402 | -0.982864 | -0.637185 | -0.514751 | -0.536553 | 0.360460 | -0.233335 | 0.226457 | 0.069525 | 0.064297 | -0.076439 | 0.138057 | -0.036769 | 0.231401 | -0.114577 | 0.319204 | -0.487769 | -0.095852 | -0.135729 | 0.610845 | ... | -0.999824 | -0.991345 | -1.0 | -0.904762 | 0.290177 | -0.668835 | -0.933443 | -0.988269 | -0.984569 | -0.983601 | -0.985328 | -0.994521 | -0.988269 | -0.999828 | -0.989040 | -0.735639 | -1.000000 | -0.257032 | -0.321591 | -0.658435 | -0.995641 | -0.994639 | -0.994202 | -0.994491 | -0.996934 | -0.995641 | -0.999974 | -0.993940 | -0.955696 | -1.000000 | 0.145844 | -0.217198 | -0.564430 | -0.212754 | -0.230622 | 0.014637 | -0.189512 | -0.852150 | 0.182170 | -0.043010 |
| 7 | 0.277432 | -0.030488 | -0.125360 | -0.996559 | -0.966728 | -0.981585 | -0.996485 | -0.966313 | -0.982982 | -0.940987 | -0.572638 | -0.817189 | 0.850328 | 0.670410 | 0.832383 | -0.976851 | -0.999980 | -0.999341 | -0.999164 | -0.995768 | -0.975847 | -0.986829 | -0.633239 | -0.743877 | -0.822408 | 0.363064 | -0.300719 | 0.346867 | -0.063694 | 0.144124 | -0.132349 | 0.083232 | 0.041631 | 0.137192 | -0.092290 | 0.372369 | -0.720913 | -0.161634 | -0.017120 | 0.561421 | ... | -0.999845 | -0.989676 | -1.0 | -0.968254 | 0.249799 | -0.655503 | -0.932563 | -0.989431 | -0.987065 | -0.985978 | -0.988193 | -0.998240 | -0.989431 | -0.999867 | -0.990035 | -0.735639 | -1.000000 | -0.197272 | -0.407284 | -0.733313 | -0.995629 | -0.994507 | -0.994727 | -0.994018 | -0.997006 | -0.995629 | -0.999974 | -0.995423 | -0.955696 | -1.000000 | 0.136382 | -0.082307 | -0.421715 | -0.020888 | 0.593996 | -0.561871 | 0.467383 | -0.851017 | 0.183779 | -0.041976 |
| 8 | 0.277293 | -0.021751 | -0.120751 | -0.997328 | -0.961245 | -0.983672 | -0.997596 | -0.957236 | -0.984379 | -0.940598 | -0.564175 | -0.823527 | 0.850328 | 0.670410 | 0.832383 | -0.983295 | -0.999985 | -0.999528 | -0.999426 | -0.997351 | -0.957212 | -0.985841 | -0.683117 | -0.524812 | -0.758524 | 0.358268 | -0.253309 | 0.266929 | -0.124250 | 0.215324 | -0.183362 | 0.171745 | -0.068957 | 0.241831 | -0.194419 | 0.216758 | -0.338026 | -0.164791 | -0.033767 | 0.724234 | ... | -0.999894 | -0.992371 | -1.0 | -1.000000 | 0.272342 | -0.750485 | -0.929474 | -0.991629 | -0.991759 | -0.991182 | -0.991522 | -0.995197 | -0.991629 | -0.999927 | -0.991428 | -0.850310 | -1.000000 | 0.073417 | -0.371389 | -0.674770 | -0.995260 | -0.996007 | -0.995828 | -0.995482 | -0.993608 | -0.995260 | -0.999977 | -0.995095 | -0.955696 | -1.000000 | 0.314038 | -0.269401 | -0.572995 | 0.012954 | 0.080936 | -0.234313 | 0.117797 | -0.847971 | 0.188982 | -0.037364 |
| 9 | 0.280586 | -0.009960 | -0.106065 | -0.994803 | -0.972758 | -0.986244 | -0.995405 | -0.973663 | -0.985642 | -0.940028 | -0.554594 | -0.815850 | 0.845442 | 0.684757 | 0.838455 | -0.986541 | -0.999963 | -0.999662 | -0.999718 | -0.996163 | -0.979809 | -0.982811 | -0.550721 | -0.294553 | -0.479711 | 0.192863 | -0.021212 | 0.037915 | 0.190540 | 0.039722 | 0.054283 | 0.037268 | 0.081551 | 0.172340 | 0.035995 | -0.184930 | 0.351156 | -0.176241 | -0.447651 | 0.391867 | ... | -0.999680 | -0.979144 | -1.0 | -1.000000 | 0.250884 | -0.510116 | -0.801919 | -0.984788 | -0.980388 | -0.981850 | -0.976771 | -0.993197 | -0.984788 | -0.999736 | -0.981969 | -0.671366 | -1.000000 | -0.139966 | 0.106785 | -0.131360 | -0.990981 | -0.990599 | -0.991665 | -0.988199 | -0.984877 | -0.990981 | -0.999929 | -0.990456 | -0.955696 | -1.000000 | 0.267383 | 0.339526 | 0.140452 | -0.020590 | -0.127730 | -0.482871 | -0.070670 | -0.848294 | 0.190310 | -0.034417 |
10 rows × 561 columns
X_train_df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 7352 entries, 0 to 7351 Data columns (total 561 columns): # Column Dtype --- ------ ----- 0 attr001 float64 1 attr002 float64 2 attr003 float64 3 attr004 float64 4 attr005 float64 5 attr006 float64 6 attr007 float64 7 attr008 float64 8 attr009 float64 9 attr010 float64 10 attr011 float64 11 attr012 float64 12 attr013 float64 13 attr014 float64 14 attr015 float64 15 attr016 float64 16 attr017 float64 17 attr018 float64 18 attr019 float64 19 attr020 float64 20 attr021 float64 21 attr022 float64 22 attr023 float64 23 attr024 float64 24 attr025 float64 25 attr026 float64 26 attr027 float64 27 attr028 float64 28 attr029 float64 29 attr030 float64 30 attr031 float64 31 attr032 float64 32 attr033 float64 33 attr034 float64 34 attr035 float64 35 attr036 float64 36 attr037 float64 37 attr038 float64 38 attr039 float64 39 attr040 float64 40 attr041 float64 41 attr042 float64 42 attr043 float64 43 attr044 float64 44 attr045 float64 45 attr046 float64 46 attr047 float64 47 attr048 float64 48 attr049 float64 49 attr050 float64 50 attr051 float64 51 attr052 float64 52 attr053 float64 53 attr054 float64 54 attr055 float64 55 attr056 float64 56 attr057 float64 57 attr058 float64 58 attr059 float64 59 attr060 float64 60 attr061 float64 61 attr062 float64 62 attr063 float64 63 attr064 float64 64 attr065 float64 65 attr066 float64 66 attr067 float64 67 attr068 float64 68 attr069 float64 69 attr070 float64 70 attr071 float64 71 attr072 float64 72 attr073 float64 73 attr074 float64 74 attr075 float64 75 attr076 float64 76 attr077 float64 77 attr078 float64 78 attr079 float64 79 attr080 float64 80 attr081 float64 81 attr082 float64 82 attr083 float64 83 attr084 float64 84 attr085 float64 85 attr086 float64 86 attr087 float64 87 attr088 float64 88 attr089 float64 89 attr090 float64 90 attr091 float64 91 attr092 float64 92 attr093 float64 93 attr094 float64 94 attr095 float64 95 attr096 float64 96 attr097 float64 97 attr098 float64 98 attr099 float64 99 attr100 float64 100 attr101 float64 101 attr102 float64 102 attr103 float64 103 attr104 float64 104 attr105 float64 105 attr106 float64 106 attr107 float64 107 attr108 float64 108 attr109 float64 109 attr110 float64 110 attr111 float64 111 attr112 float64 112 attr113 float64 113 attr114 float64 114 attr115 float64 115 attr116 float64 116 attr117 float64 117 attr118 float64 118 attr119 float64 119 attr120 float64 120 attr121 float64 121 attr122 float64 122 attr123 float64 123 attr124 float64 124 attr125 float64 125 attr126 float64 126 attr127 float64 127 attr128 float64 128 attr129 float64 129 attr130 float64 130 attr131 float64 131 attr132 float64 132 attr133 float64 133 attr134 float64 134 attr135 float64 135 attr136 float64 136 attr137 float64 137 attr138 float64 138 attr139 float64 139 attr140 float64 140 attr141 float64 141 attr142 float64 142 attr143 float64 143 attr144 float64 144 attr145 float64 145 attr146 float64 146 attr147 float64 147 attr148 float64 148 attr149 float64 149 attr150 float64 150 attr151 float64 151 attr152 float64 152 attr153 float64 153 attr154 float64 154 attr155 float64 155 attr156 float64 156 attr157 float64 157 attr158 float64 158 attr159 float64 159 attr160 float64 160 attr161 float64 161 attr162 float64 162 attr163 float64 163 attr164 float64 164 attr165 float64 165 attr166 float64 166 attr167 float64 167 attr168 float64 168 attr169 float64 169 attr170 float64 170 attr171 float64 171 attr172 float64 172 attr173 float64 173 attr174 float64 174 attr175 float64 175 attr176 float64 176 attr177 float64 177 attr178 float64 178 attr179 float64 179 attr180 float64 180 attr181 float64 181 attr182 float64 182 attr183 float64 183 attr184 float64 184 attr185 float64 185 attr186 float64 186 attr187 float64 187 attr188 float64 188 attr189 float64 189 attr190 float64 190 attr191 float64 191 attr192 float64 192 attr193 float64 193 attr194 float64 194 attr195 float64 195 attr196 float64 196 attr197 float64 197 attr198 float64 198 attr199 float64 199 attr200 float64 200 attr201 float64 201 attr202 float64 202 attr203 float64 203 attr204 float64 204 attr205 float64 205 attr206 float64 206 attr207 float64 207 attr208 float64 208 attr209 float64 209 attr210 float64 210 attr211 float64 211 attr212 float64 212 attr213 float64 213 attr214 float64 214 attr215 float64 215 attr216 float64 216 attr217 float64 217 attr218 float64 218 attr219 float64 219 attr220 float64 220 attr221 float64 221 attr222 float64 222 attr223 float64 223 attr224 float64 224 attr225 float64 225 attr226 float64 226 attr227 float64 227 attr228 float64 228 attr229 float64 229 attr230 float64 230 attr231 float64 231 attr232 float64 232 attr233 float64 233 attr234 float64 234 attr235 float64 235 attr236 float64 236 attr237 float64 237 attr238 float64 238 attr239 float64 239 attr240 float64 240 attr241 float64 241 attr242 float64 242 attr243 float64 243 attr244 float64 244 attr245 float64 245 attr246 float64 246 attr247 float64 247 attr248 float64 248 attr249 float64 249 attr250 float64 250 attr251 float64 251 attr252 float64 252 attr253 float64 253 attr254 float64 254 attr255 float64 255 attr256 float64 256 attr257 float64 257 attr258 float64 258 attr259 float64 259 attr260 float64 260 attr261 float64 261 attr262 float64 262 attr263 float64 263 attr264 float64 264 attr265 float64 265 attr266 float64 266 attr267 float64 267 attr268 float64 268 attr269 float64 269 attr270 float64 270 attr271 float64 271 attr272 float64 272 attr273 float64 273 attr274 float64 274 attr275 float64 275 attr276 float64 276 attr277 float64 277 attr278 float64 278 attr279 float64 279 attr280 float64 280 attr281 float64 281 attr282 float64 282 attr283 float64 283 attr284 float64 284 attr285 float64 285 attr286 float64 286 attr287 float64 287 attr288 float64 288 attr289 float64 289 attr290 float64 290 attr291 float64 291 attr292 float64 292 attr293 float64 293 attr294 float64 294 attr295 float64 295 attr296 float64 296 attr297 float64 297 attr298 float64 298 attr299 float64 299 attr300 float64 300 attr301 float64 301 attr302 float64 302 attr303 float64 303 attr304 float64 304 attr305 float64 305 attr306 float64 306 attr307 float64 307 attr308 float64 308 attr309 float64 309 attr310 float64 310 attr311 float64 311 attr312 float64 312 attr313 float64 313 attr314 float64 314 attr315 float64 315 attr316 float64 316 attr317 float64 317 attr318 float64 318 attr319 float64 319 attr320 float64 320 attr321 float64 321 attr322 float64 322 attr323 float64 323 attr324 float64 324 attr325 float64 325 attr326 float64 326 attr327 float64 327 attr328 float64 328 attr329 float64 329 attr330 float64 330 attr331 float64 331 attr332 float64 332 attr333 float64 333 attr334 float64 334 attr335 float64 335 attr336 float64 336 attr337 float64 337 attr338 float64 338 attr339 float64 339 attr340 float64 340 attr341 float64 341 attr342 float64 342 attr343 float64 343 attr344 float64 344 attr345 float64 345 attr346 float64 346 attr347 float64 347 attr348 float64 348 attr349 float64 349 attr350 float64 350 attr351 float64 351 attr352 float64 352 attr353 float64 353 attr354 float64 354 attr355 float64 355 attr356 float64 356 attr357 float64 357 attr358 float64 358 attr359 float64 359 attr360 float64 360 attr361 float64 361 attr362 float64 362 attr363 float64 363 attr364 float64 364 attr365 float64 365 attr366 float64 366 attr367 float64 367 attr368 float64 368 attr369 float64 369 attr370 float64 370 attr371 float64 371 attr372 float64 372 attr373 float64 373 attr374 float64 374 attr375 float64 375 attr376 float64 376 attr377 float64 377 attr378 float64 378 attr379 float64 379 attr380 float64 380 attr381 float64 381 attr382 float64 382 attr383 float64 383 attr384 float64 384 attr385 float64 385 attr386 float64 386 attr387 float64 387 attr388 float64 388 attr389 float64 389 attr390 float64 390 attr391 float64 391 attr392 float64 392 attr393 float64 393 attr394 float64 394 attr395 float64 395 attr396 float64 396 attr397 float64 397 attr398 float64 398 attr399 float64 399 attr400 float64 400 attr401 float64 401 attr402 float64 402 attr403 float64 403 attr404 float64 404 attr405 float64 405 attr406 float64 406 attr407 float64 407 attr408 float64 408 attr409 float64 409 attr410 float64 410 attr411 float64 411 attr412 float64 412 attr413 float64 413 attr414 float64 414 attr415 float64 415 attr416 float64 416 attr417 float64 417 attr418 float64 418 attr419 float64 419 attr420 float64 420 attr421 float64 421 attr422 float64 422 attr423 float64 423 attr424 float64 424 attr425 float64 425 attr426 float64 426 attr427 float64 427 attr428 float64 428 attr429 float64 429 attr430 float64 430 attr431 float64 431 attr432 float64 432 attr433 float64 433 attr434 float64 434 attr435 float64 435 attr436 float64 436 attr437 float64 437 attr438 float64 438 attr439 float64 439 attr440 float64 440 attr441 float64 441 attr442 float64 442 attr443 float64 443 attr444 float64 444 attr445 float64 445 attr446 float64 446 attr447 float64 447 attr448 float64 448 attr449 float64 449 attr450 float64 450 attr451 float64 451 attr452 float64 452 attr453 float64 453 attr454 float64 454 attr455 float64 455 attr456 float64 456 attr457 float64 457 attr458 float64 458 attr459 float64 459 attr460 float64 460 attr461 float64 461 attr462 float64 462 attr463 float64 463 attr464 float64 464 attr465 float64 465 attr466 float64 466 attr467 float64 467 attr468 float64 468 attr469 float64 469 attr470 float64 470 attr471 float64 471 attr472 float64 472 attr473 float64 473 attr474 float64 474 attr475 float64 475 attr476 float64 476 attr477 float64 477 attr478 float64 478 attr479 float64 479 attr480 float64 480 attr481 float64 481 attr482 float64 482 attr483 float64 483 attr484 float64 484 attr485 float64 485 attr486 float64 486 attr487 float64 487 attr488 float64 488 attr489 float64 489 attr490 float64 490 attr491 float64 491 attr492 float64 492 attr493 float64 493 attr494 float64 494 attr495 float64 495 attr496 float64 496 attr497 float64 497 attr498 float64 498 attr499 float64 499 attr500 float64 500 attr501 float64 501 attr502 float64 502 attr503 float64 503 attr504 float64 504 attr505 float64 505 attr506 float64 506 attr507 float64 507 attr508 float64 508 attr509 float64 509 attr510 float64 510 attr511 float64 511 attr512 float64 512 attr513 float64 513 attr514 float64 514 attr515 float64 515 attr516 float64 516 attr517 float64 517 attr518 float64 518 attr519 float64 519 attr520 float64 520 attr521 float64 521 attr522 float64 522 attr523 float64 523 attr524 float64 524 attr525 float64 525 attr526 float64 526 attr527 float64 527 attr528 float64 528 attr529 float64 529 attr530 float64 530 attr531 float64 531 attr532 float64 532 attr533 float64 533 attr534 float64 534 attr535 float64 535 attr536 float64 536 attr537 float64 537 attr538 float64 538 attr539 float64 539 attr540 float64 540 attr541 float64 541 attr542 float64 542 attr543 float64 543 attr544 float64 544 attr545 float64 545 attr546 float64 546 attr547 float64 547 attr548 float64 548 attr549 float64 549 attr550 float64 550 attr551 float64 551 attr552 float64 552 attr553 float64 553 attr554 float64 554 attr555 float64 555 attr556 float64 556 attr557 float64 557 attr558 float64 558 attr559 float64 559 attr560 float64 560 attr561 float64 dtypes: float64(561) memory usage: 31.5 MB
X_train_df.describe()
| attr001 | attr002 | attr003 | attr004 | attr005 | attr006 | attr007 | attr008 | attr009 | attr010 | attr011 | attr012 | attr013 | attr014 | attr015 | attr016 | attr017 | attr018 | attr019 | attr020 | attr021 | attr022 | attr023 | attr024 | attr025 | attr026 | attr027 | attr028 | attr029 | attr030 | attr031 | attr032 | attr033 | attr034 | attr035 | attr036 | attr037 | attr038 | attr039 | attr040 | ... | attr522 | attr523 | attr524 | attr525 | attr526 | attr527 | attr528 | attr529 | attr530 | attr531 | attr532 | attr533 | attr534 | attr535 | attr536 | attr537 | attr538 | attr539 | attr540 | attr541 | attr542 | attr543 | attr544 | attr545 | attr546 | attr547 | attr548 | attr549 | attr550 | attr551 | attr552 | attr553 | attr554 | attr555 | attr556 | attr557 | attr558 | attr559 | attr560 | attr561 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | ... | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 | 7352.000000 |
| mean | 0.274488 | -0.017695 | -0.109141 | -0.605438 | -0.510938 | -0.604754 | -0.630512 | -0.526907 | -0.606150 | -0.468604 | -0.306043 | -0.557121 | 0.523551 | 0.387386 | 0.594374 | -0.547569 | -0.820041 | -0.901874 | -0.845784 | -0.684345 | -0.643770 | -0.631069 | -0.102993 | -0.137937 | -0.163946 | -0.116599 | 0.102762 | -0.037786 | 0.130477 | -0.026229 | 0.026322 | 0.159966 | -0.019575 | 0.009420 | 0.033291 | 0.036587 | -0.078640 | -0.125131 | -0.193802 | 0.105005 | ... | -0.842188 | -0.678618 | -0.347657 | -0.877995 | 0.178195 | -0.312968 | -0.615441 | -0.693210 | -0.692876 | -0.674830 | -0.726645 | -0.885103 | -0.693210 | -0.874292 | -0.719795 | -0.087878 | -0.889442 | -0.046516 | -0.253649 | -0.565425 | -0.779376 | -0.792391 | -0.772836 | -0.811409 | -0.871927 | -0.779376 | -0.935785 | -0.771497 | -0.284627 | -0.898859 | 0.125293 | -0.307009 | -0.625294 | 0.008684 | 0.002186 | 0.008726 | -0.005981 | -0.489547 | 0.058593 | -0.056515 |
| std | 0.070261 | 0.040811 | 0.056635 | 0.448734 | 0.502645 | 0.418687 | 0.424073 | 0.485942 | 0.414122 | 0.544547 | 0.282243 | 0.293867 | 0.363594 | 0.343611 | 0.297818 | 0.471808 | 0.259607 | 0.126333 | 0.221983 | 0.371608 | 0.371581 | 0.386569 | 0.468959 | 0.437268 | 0.371363 | 0.306507 | 0.246593 | 0.243635 | 0.230067 | 0.257383 | 0.215001 | 0.208837 | 0.221432 | 0.286081 | 0.216289 | 0.236226 | 0.232757 | 0.363155 | 0.331122 | 0.385379 | ... | 0.230580 | 0.370612 | 0.670112 | 0.188636 | 0.253755 | 0.358631 | 0.345329 | 0.335026 | 0.322850 | 0.343454 | 0.293098 | 0.173574 | 0.335026 | 0.192684 | 0.318706 | 0.611793 | 0.157653 | 0.282665 | 0.326624 | 0.326620 | 0.275733 | 0.265434 | 0.287613 | 0.246680 | 0.193344 | 0.275733 | 0.138683 | 0.287577 | 0.630896 | 0.143135 | 0.250994 | 0.321011 | 0.307584 | 0.336787 | 0.448306 | 0.608303 | 0.477975 | 0.511807 | 0.297480 | 0.279122 |
| min | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.999873 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.999999 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.925897 | -0.963099 | -1.000000 | -0.822053 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.753754 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.972219 | ... | -1.000000 | -0.999499 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.997500 | -1.000000 | -1.000000 | -0.999996 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.999996 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -0.995357 | -0.999765 | -0.976580 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | -1.000000 |
| 25% | 0.262975 | -0.024863 | -0.120993 | -0.992754 | -0.978129 | -0.980233 | -0.993591 | -0.978162 | -0.980251 | -0.936219 | -0.563561 | -0.812744 | 0.197051 | 0.101829 | 0.389787 | -0.982992 | -0.999936 | -0.999786 | -0.999460 | -0.994387 | -0.982159 | -0.979623 | -0.573441 | -0.559584 | -0.505512 | -0.364926 | -0.082544 | -0.190581 | -0.023857 | -0.221943 | -0.135433 | 0.034430 | -0.168841 | -0.207296 | -0.123514 | -0.106542 | -0.238877 | -0.373937 | -0.403511 | -0.147970 | ... | -0.999862 | -0.989044 | -1.000000 | -0.968254 | -0.000409 | -0.607485 | -0.882254 | -0.984123 | -0.980329 | -0.980453 | -0.981029 | -0.994189 | -0.984123 | -0.999725 | -0.986633 | -0.690397 | -1.000000 | -0.240882 | -0.492028 | -0.802207 | -0.993100 | -0.993508 | -0.992736 | -0.994355 | -0.994065 | -0.993100 | -0.999957 | -0.992268 | -0.955696 | -0.968254 | -0.023692 | -0.542602 | -0.845573 | -0.121527 | -0.289549 | -0.482273 | -0.376341 | -0.812065 | -0.017885 | -0.143414 |
| 50% | 0.277193 | -0.017219 | -0.108676 | -0.946196 | -0.851897 | -0.859365 | -0.950709 | -0.857328 | -0.857143 | -0.881637 | -0.479677 | -0.736516 | 0.792060 | 0.627737 | 0.778059 | -0.885461 | -0.998046 | -0.994065 | -0.985546 | -0.957859 | -0.896093 | -0.864515 | -0.073369 | -0.136793 | -0.148889 | -0.129393 | 0.070073 | -0.019001 | 0.134149 | -0.040710 | 0.011748 | 0.168444 | -0.022448 | 0.029044 | 0.003266 | 0.049432 | -0.081940 | -0.163728 | -0.189673 | 0.147482 | ... | -0.997494 | -0.950502 | -0.740806 | -0.904762 | 0.168645 | -0.364784 | -0.727332 | -0.886439 | -0.837698 | -0.859053 | -0.836296 | -0.960136 | -0.886439 | -0.986266 | -0.923298 | -0.190223 | -0.948718 | -0.061597 | -0.309252 | -0.655594 | -0.952398 | -0.947140 | -0.945223 | -0.950796 | -0.975676 | -0.952398 | -0.998471 | -0.949768 | -0.455569 | -0.904762 | 0.134000 | -0.343685 | -0.711692 | 0.009509 | 0.008943 | 0.008735 | -0.000368 | -0.709417 | 0.182071 | 0.003181 |
| 75% | 0.288461 | -0.010783 | -0.097794 | -0.242813 | -0.034231 | -0.262415 | -0.292680 | -0.066701 | -0.265671 | -0.017129 | -0.065364 | -0.332014 | 0.844420 | 0.685622 | 0.837323 | -0.107428 | -0.710707 | -0.816703 | -0.748018 | -0.393220 | -0.310548 | -0.316037 | 0.336504 | 0.280170 | 0.164123 | 0.132657 | 0.276872 | 0.128635 | 0.285318 | 0.172344 | 0.177832 | 0.293410 | 0.130862 | 0.230983 | 0.175602 | 0.195556 | 0.079229 | 0.070818 | 0.005170 | 0.382231 | ... | -0.731425 | -0.371989 | 0.350640 | -0.873016 | 0.364240 | -0.082569 | -0.449997 | -0.438439 | -0.451900 | -0.397123 | -0.540666 | -0.837425 | -0.438439 | -0.800743 | -0.484202 | 0.515789 | -0.846154 | 0.148795 | -0.069952 | -0.428376 | -0.611952 | -0.642627 | -0.608047 | -0.688776 | -0.808263 | -0.611952 | -0.922503 | -0.603952 | 0.336785 | -0.873016 | 0.289096 | -0.126979 | -0.503878 | 0.150865 | 0.292861 | 0.506187 | 0.359368 | -0.509079 | 0.248353 | 0.107659 |
| max | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.916238 | 1.000000 | 1.000000 | 0.967664 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.919662 | 1.000000 | 1.000000 | 1.000000 | 0.978449 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.814623 | 1.000000 | 0.997207 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.975821 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.842119 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.969311 | 0.949350 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.968254 | 0.946700 | 0.989538 | 0.956845 | 1.000000 | 1.000000 | 0.998702 | 0.996078 | 1.000000 | 0.478157 | 1.000000 |
8 rows × 561 columns
Xy_train_df.groupby('targetVar').size()
targetVar 1 1226 2 1073 3 986 4 1286 5 1374 6 1407 dtype: int64
# Histograms for each attribute
X_train_df.hist(layout=(dispRow,dispCol))
plt.show()
# Box and Whisker plot for each attribute
X_train_df.plot(kind='box', subplots=True, layout=(dispRow,dispCol))
plt.show()
# Correlation matrix
# fig = plt.figure(figsize=(16,12))
# ax = fig.add_subplot(111)
# correlations = X_train_df.corr(method='pearson')
# cax = ax.matshow(correlations, vmin=-1, vmax=1)
# fig.colorbar(cax)
# plt.show()
print("X_train_df.shape: {} y_train_df.shape: {}".format(X_train_df.shape, y_train_df.shape))
print("X_test_df.shape: {} y_test_df.shape: {}".format(X_test_df.shape, y_test_df.shape))
X_train_df.shape: (7352, 561) y_train_df.shape: (7352, 1) X_test_df.shape: (2947, 561) y_test_df.shape: (2947, 1)
# Not applicable for this iteration of the project
# Not applicable for this iteration of the project
# Not applicable for this iteration of the project
# Finalize the training and testing datasets for the modeling activities
X_train = X_train_df.to_numpy()
y_train = y_train_df.to_numpy().ravel()
X_test = X_test_df.to_numpy()
y_test = y_test_df.to_numpy().ravel()
print("X_train.shape: {} y_train.shape: {}".format(X_train.shape, y_train.shape))
print("X_test.shape: {} y_test.shape: {}".format(X_test.shape, y_test.shape))
X_train.shape: (7352, 561) y_train.shape: (7352,) X_test.shape: (2947, 561) y_test.shape: (2947,)
# Set up Algorithms Spot-Checking Array
startTimeModule = datetime.now()
train_models = []
train_results = []
train_model_names = []
train_metrics = []
# train_models.append(('LGR', LogisticRegression(random_state=seedNum)))
# train_models.append(('CART', DecisionTreeClassifier(random_state=seedNum)))
# train_models.append(('KNN', KNeighborsClassifier(n_jobs=n_jobs)))
# train_models.append(('BGT', BaggingClassifier(random_state=seedNum, n_jobs=n_jobs)))
# train_models.append(('RNF', RandomForestClassifier(random_state=seedNum, n_jobs=n_jobs)))
# train_models.append(('EXT', ExtraTreesClassifier(random_state=seedNum, n_jobs=n_jobs)))
# train_models.append(('GBM', GradientBoostingClassifier(random_state=seedNum)))
train_models.append(('XGB', XGBClassifier(random_state=seedNum, objective='multi:softmax', num_class=6, tree_method='gpu_hist')))
# Generate model in turn
for name, model in train_models:
if notifyStatus: email_notify("Algorithm "+name+" modeling has begun! "+datetime.now().strftime('%a %B %d, %Y %I:%M:%S %p'))
startTimeModule = datetime.now()
kfold = KFold(n_splits=n_folds, shuffle=True, random_state=seedNum)
cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring, verbose=1)
train_results.append(cv_results)
train_model_names.append(name)
train_metrics.append(cv_results.mean())
print("%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()))
print(model)
print ('Model training time:', (datetime.now() - startTimeModule), '\n')
if notifyStatus: email_notify("Algorithm "+name+" modeling completed! "+datetime.now().strftime('%a %B %d, %Y %I:%M:%S %p'))
print ('Average metrics ('+scoring+') from all models:',np.mean(train_metrics))
print ('Total training time for all models:',(datetime.now() - startTimeModule))
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
XGB: 0.987894 (0.002699)
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, num_class=6, objective='multi:softmax',
random_state=888, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
seed=None, silent=None, subsample=1, tree_method='gpu_hist',
verbosity=1)
Model training time: 0:00:10.067192
Average metrics (accuracy) from all models: 0.9878943936514102
Total training time for all models: 0:00:10.067936
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 10.1s finished
fig = plt.figure(figsize=(16,12))
fig.suptitle('Algorithm Comparison - Spot Checking')
ax = fig.add_subplot(111)
plt.boxplot(train_results)
ax.set_xticklabels(train_model_names)
plt.show()
# Set up the comparison array
tune_results = []
tune_model_names = []
# Tuning XGBoost n_estimators, max_depth, and min_child_weight
startTimeModule = datetime.now()
if notifyStatus: email_notify("Algorithm tuning iteration #1 has begun! "+datetime.now().strftime('%a %B %d, %Y %I:%M:%S %p'))
tune_model1 = XGBClassifier(random_state=seedNum, objective='multi:softmax', num_class=6, tree_method='gpu_hist')
tune_model_names.append('XGB_1')
paramGrid1 = dict(n_estimators=range(100,1001,100), max_depth=np.array([3,6,9]), min_child_weight=np.array([1,2,3]))
kfold = KFold(n_splits=n_folds, shuffle=True, random_state=seedNum)
grid1 = GridSearchCV(estimator=tune_model1, param_grid=paramGrid1, scoring=scoring, cv=kfold, verbose=2)
grid_result1 = grid1.fit(X_train, y_train)
print("Best: %f using %s" % (grid_result1.best_score_, grid_result1.best_params_))
tune_results.append(grid_result1.cv_results_['mean_test_score'])
means = grid_result1.cv_results_['mean_test_score']
stds = grid_result1.cv_results_['std_test_score']
params = grid_result1.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
print ('Model training time:',(datetime.now() - startTimeModule))
if notifyStatus: email_notify("Algorithm tuning iteration #1 completed! "+datetime.now().strftime('%a %B %d, %Y %I:%M:%S %p'))
Fitting 5 folds for each of 90 candidates, totalling 450 fits [CV] max_depth=3, min_child_weight=1, n_estimators=100 ...............
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] max_depth=3, min_child_weight=1, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=1, n_estimators=100 ...............
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.8s remaining: 0.0s
[CV] max_depth=3, min_child_weight=1, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=200, total= 3.1s [CV] max_depth=3, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=200, total= 3.1s [CV] max_depth=3, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=200, total= 3.1s [CV] max_depth=3, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=200, total= 3.1s [CV] max_depth=3, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=200, total= 3.1s [CV] max_depth=3, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=300, total= 4.3s [CV] max_depth=3, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=300, total= 4.3s [CV] max_depth=3, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=300, total= 4.3s [CV] max_depth=3, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=300, total= 4.3s [CV] max_depth=3, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=300, total= 4.3s [CV] max_depth=3, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=400, total= 5.4s [CV] max_depth=3, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=400, total= 5.4s [CV] max_depth=3, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=400, total= 5.4s [CV] max_depth=3, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=400, total= 5.4s [CV] max_depth=3, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=400, total= 5.4s [CV] max_depth=3, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=500, total= 6.5s [CV] max_depth=3, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=500, total= 6.5s [CV] max_depth=3, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=500, total= 6.5s [CV] max_depth=3, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=500, total= 6.5s [CV] max_depth=3, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=500, total= 6.5s [CV] max_depth=3, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=600, total= 7.5s [CV] max_depth=3, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=600, total= 7.5s [CV] max_depth=3, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=600, total= 7.5s [CV] max_depth=3, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=600, total= 7.5s [CV] max_depth=3, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=600, total= 7.5s [CV] max_depth=3, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=700, total= 8.5s [CV] max_depth=3, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=700, total= 8.5s [CV] max_depth=3, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=700, total= 8.5s [CV] max_depth=3, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=700, total= 8.5s [CV] max_depth=3, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=700, total= 8.5s [CV] max_depth=3, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=800, total= 9.5s [CV] max_depth=3, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=800, total= 9.5s [CV] max_depth=3, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=800, total= 9.5s [CV] max_depth=3, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=800, total= 9.5s [CV] max_depth=3, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=800, total= 9.5s [CV] max_depth=3, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=900, total= 10.5s [CV] max_depth=3, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=900, total= 10.5s [CV] max_depth=3, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=900, total= 10.5s [CV] max_depth=3, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=900, total= 10.5s [CV] max_depth=3, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=1, n_estimators=900, total= 10.5s [CV] max_depth=3, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=1, n_estimators=1000, total= 11.5s [CV] max_depth=3, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=1, n_estimators=1000, total= 11.5s [CV] max_depth=3, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=1, n_estimators=1000, total= 11.4s [CV] max_depth=3, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=1, n_estimators=1000, total= 11.5s [CV] max_depth=3, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=1, n_estimators=1000, total= 11.4s [CV] max_depth=3, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=300, total= 4.2s [CV] max_depth=3, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=300, total= 4.2s [CV] max_depth=3, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=300, total= 4.2s [CV] max_depth=3, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=300, total= 4.2s [CV] max_depth=3, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=300, total= 4.2s [CV] max_depth=3, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=500, total= 6.3s [CV] max_depth=3, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=500, total= 6.3s [CV] max_depth=3, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=500, total= 6.3s [CV] max_depth=3, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=600, total= 7.3s [CV] max_depth=3, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=600, total= 7.3s [CV] max_depth=3, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=600, total= 7.3s [CV] max_depth=3, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=600, total= 7.3s [CV] max_depth=3, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=600, total= 7.3s [CV] max_depth=3, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=700, total= 8.3s [CV] max_depth=3, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=700, total= 8.3s [CV] max_depth=3, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=700, total= 8.3s [CV] max_depth=3, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=700, total= 8.3s [CV] max_depth=3, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=700, total= 8.2s [CV] max_depth=3, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=800, total= 9.2s [CV] max_depth=3, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=800, total= 9.2s [CV] max_depth=3, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=800, total= 9.2s [CV] max_depth=3, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=800, total= 9.2s [CV] max_depth=3, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=800, total= 9.2s [CV] max_depth=3, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=900, total= 10.2s [CV] max_depth=3, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=900, total= 10.2s [CV] max_depth=3, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=900, total= 10.1s [CV] max_depth=3, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=900, total= 10.2s [CV] max_depth=3, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=2, n_estimators=900, total= 10.2s [CV] max_depth=3, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=2, n_estimators=1000, total= 11.1s [CV] max_depth=3, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=2, n_estimators=1000, total= 11.1s [CV] max_depth=3, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=2, n_estimators=1000, total= 11.1s [CV] max_depth=3, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=2, n_estimators=1000, total= 11.1s [CV] max_depth=3, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=2, n_estimators=1000, total= 11.1s [CV] max_depth=3, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=100, total= 1.8s [CV] max_depth=3, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=200, total= 3.0s [CV] max_depth=3, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=300, total= 4.1s [CV] max_depth=3, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=300, total= 4.1s [CV] max_depth=3, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=300, total= 4.1s [CV] max_depth=3, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=300, total= 4.1s [CV] max_depth=3, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=300, total= 4.1s [CV] max_depth=3, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=400, total= 5.2s [CV] max_depth=3, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=400, total= 5.1s [CV] max_depth=3, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=500, total= 6.2s [CV] max_depth=3, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=600, total= 7.2s [CV] max_depth=3, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=600, total= 7.2s [CV] max_depth=3, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=600, total= 7.1s [CV] max_depth=3, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=600, total= 7.2s [CV] max_depth=3, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=600, total= 7.2s [CV] max_depth=3, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=700, total= 8.1s [CV] max_depth=3, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=700, total= 8.1s [CV] max_depth=3, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=700, total= 8.1s [CV] max_depth=3, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=700, total= 8.1s [CV] max_depth=3, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=700, total= 8.1s [CV] max_depth=3, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=800, total= 9.1s [CV] max_depth=3, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=800, total= 9.1s [CV] max_depth=3, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=800, total= 9.1s [CV] max_depth=3, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=800, total= 9.1s [CV] max_depth=3, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=800, total= 9.1s [CV] max_depth=3, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=900, total= 10.0s [CV] max_depth=3, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=900, total= 10.1s [CV] max_depth=3, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=900, total= 10.0s [CV] max_depth=3, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=900, total= 10.1s [CV] max_depth=3, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=3, min_child_weight=3, n_estimators=900, total= 10.0s [CV] max_depth=3, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=3, n_estimators=1000, total= 11.0s [CV] max_depth=3, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=3, n_estimators=1000, total= 11.0s [CV] max_depth=3, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=3, n_estimators=1000, total= 11.0s [CV] max_depth=3, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=3, n_estimators=1000, total= 11.0s [CV] max_depth=3, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=3, min_child_weight=3, n_estimators=1000, total= 11.0s [CV] max_depth=6, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=100, total= 2.7s [CV] max_depth=6, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=100, total= 2.8s [CV] max_depth=6, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=100, total= 2.7s [CV] max_depth=6, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=100, total= 2.8s [CV] max_depth=6, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=100, total= 2.8s [CV] max_depth=6, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=200, total= 4.1s [CV] max_depth=6, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=200, total= 4.1s [CV] max_depth=6, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=200, total= 4.1s [CV] max_depth=6, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=200, total= 4.1s [CV] max_depth=6, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=200, total= 4.1s [CV] max_depth=6, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=300, total= 5.3s [CV] max_depth=6, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=300, total= 5.3s [CV] max_depth=6, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=300, total= 5.2s [CV] max_depth=6, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=300, total= 5.3s [CV] max_depth=6, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=300, total= 5.3s [CV] max_depth=6, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=400, total= 6.3s [CV] max_depth=6, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=400, total= 6.3s [CV] max_depth=6, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=400, total= 6.3s [CV] max_depth=6, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=400, total= 6.4s [CV] max_depth=6, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=400, total= 6.3s [CV] max_depth=6, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=500, total= 7.3s [CV] max_depth=6, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=500, total= 7.3s [CV] max_depth=6, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=500, total= 7.3s [CV] max_depth=6, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=500, total= 7.4s [CV] max_depth=6, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=500, total= 7.3s [CV] max_depth=6, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=600, total= 8.3s [CV] max_depth=6, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=600, total= 8.3s [CV] max_depth=6, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=600, total= 8.3s [CV] max_depth=6, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=600, total= 8.4s [CV] max_depth=6, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=600, total= 8.3s [CV] max_depth=6, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=700, total= 9.3s [CV] max_depth=6, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=700, total= 9.3s [CV] max_depth=6, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=700, total= 9.2s [CV] max_depth=6, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=700, total= 9.3s [CV] max_depth=6, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=700, total= 9.3s [CV] max_depth=6, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=800, total= 10.2s [CV] max_depth=6, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=800, total= 10.3s [CV] max_depth=6, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=800, total= 10.2s [CV] max_depth=6, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=800, total= 10.3s [CV] max_depth=6, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=800, total= 10.3s [CV] max_depth=6, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=900, total= 11.2s [CV] max_depth=6, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=900, total= 11.2s [CV] max_depth=6, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=900, total= 11.2s [CV] max_depth=6, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=900, total= 11.2s [CV] max_depth=6, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=1, n_estimators=900, total= 11.2s [CV] max_depth=6, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=1, n_estimators=1000, total= 12.1s [CV] max_depth=6, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=1, n_estimators=1000, total= 12.2s [CV] max_depth=6, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=1, n_estimators=1000, total= 12.1s [CV] max_depth=6, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=1, n_estimators=1000, total= 12.2s [CV] max_depth=6, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=1, n_estimators=1000, total= 12.2s [CV] max_depth=6, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=100, total= 2.6s [CV] max_depth=6, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=100, total= 2.6s [CV] max_depth=6, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=100, total= 2.6s [CV] max_depth=6, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=100, total= 2.6s [CV] max_depth=6, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=200, total= 3.8s [CV] max_depth=6, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=200, total= 3.8s [CV] max_depth=6, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=200, total= 3.8s [CV] max_depth=6, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=200, total= 3.8s [CV] max_depth=6, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=200, total= 3.8s [CV] max_depth=6, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=300, total= 4.9s [CV] max_depth=6, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=300, total= 4.9s [CV] max_depth=6, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=300, total= 4.9s [CV] max_depth=6, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=300, total= 4.9s [CV] max_depth=6, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=300, total= 4.9s [CV] max_depth=6, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=400, total= 5.9s [CV] max_depth=6, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=400, total= 5.9s [CV] max_depth=6, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=400, total= 5.9s [CV] max_depth=6, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=400, total= 5.9s [CV] max_depth=6, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=400, total= 5.9s [CV] max_depth=6, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=500, total= 6.9s [CV] max_depth=6, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=500, total= 6.9s [CV] max_depth=6, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=500, total= 6.8s [CV] max_depth=6, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=500, total= 6.9s [CV] max_depth=6, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=500, total= 6.9s [CV] max_depth=6, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=600, total= 7.8s [CV] max_depth=6, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=600, total= 7.9s [CV] max_depth=6, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=600, total= 7.8s [CV] max_depth=6, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=600, total= 7.9s [CV] max_depth=6, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=600, total= 7.8s [CV] max_depth=6, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=700, total= 8.8s [CV] max_depth=6, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=700, total= 8.8s [CV] max_depth=6, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=700, total= 8.8s [CV] max_depth=6, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=700, total= 8.8s [CV] max_depth=6, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=700, total= 8.8s [CV] max_depth=6, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=800, total= 9.8s [CV] max_depth=6, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=800, total= 9.8s [CV] max_depth=6, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=800, total= 9.7s [CV] max_depth=6, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=800, total= 9.8s [CV] max_depth=6, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=800, total= 9.8s [CV] max_depth=6, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=900, total= 10.7s [CV] max_depth=6, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=900, total= 10.7s [CV] max_depth=6, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=900, total= 10.7s [CV] max_depth=6, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=900, total= 10.8s [CV] max_depth=6, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=2, n_estimators=900, total= 10.7s [CV] max_depth=6, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=2, n_estimators=1000, total= 11.7s [CV] max_depth=6, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=2, n_estimators=1000, total= 11.7s [CV] max_depth=6, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=2, n_estimators=1000, total= 11.6s [CV] max_depth=6, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=2, n_estimators=1000, total= 11.7s [CV] max_depth=6, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=2, n_estimators=1000, total= 11.7s [CV] max_depth=6, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=100, total= 2.5s [CV] max_depth=6, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=200, total= 3.7s [CV] max_depth=6, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=200, total= 3.7s [CV] max_depth=6, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=200, total= 3.7s [CV] max_depth=6, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=200, total= 3.7s [CV] max_depth=6, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=200, total= 3.7s [CV] max_depth=6, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=300, total= 4.7s [CV] max_depth=6, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=300, total= 4.8s [CV] max_depth=6, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=300, total= 4.7s [CV] max_depth=6, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=300, total= 4.8s [CV] max_depth=6, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=300, total= 4.7s [CV] max_depth=6, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=400, total= 5.7s [CV] max_depth=6, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=400, total= 5.7s [CV] max_depth=6, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=400, total= 5.7s [CV] max_depth=6, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=400, total= 5.7s [CV] max_depth=6, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=400, total= 5.7s [CV] max_depth=6, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=500, total= 6.7s [CV] max_depth=6, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=500, total= 6.7s [CV] max_depth=6, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=500, total= 6.7s [CV] max_depth=6, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=500, total= 6.7s [CV] max_depth=6, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=500, total= 6.7s [CV] max_depth=6, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=600, total= 7.7s [CV] max_depth=6, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=600, total= 7.7s [CV] max_depth=6, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=600, total= 7.6s [CV] max_depth=6, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=600, total= 7.7s [CV] max_depth=6, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=600, total= 7.7s [CV] max_depth=6, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=700, total= 8.6s [CV] max_depth=6, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=700, total= 8.7s [CV] max_depth=6, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=700, total= 8.6s [CV] max_depth=6, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=700, total= 8.6s [CV] max_depth=6, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=700, total= 8.6s [CV] max_depth=6, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=800, total= 9.6s [CV] max_depth=6, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=800, total= 9.6s [CV] max_depth=6, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=800, total= 9.5s [CV] max_depth=6, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=800, total= 9.6s [CV] max_depth=6, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=800, total= 9.6s [CV] max_depth=6, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=900, total= 10.5s [CV] max_depth=6, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=900, total= 10.5s [CV] max_depth=6, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=900, total= 10.5s [CV] max_depth=6, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=900, total= 10.6s [CV] max_depth=6, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=6, min_child_weight=3, n_estimators=900, total= 10.6s [CV] max_depth=6, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=3, n_estimators=1000, total= 11.5s [CV] max_depth=6, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=3, n_estimators=1000, total= 11.5s [CV] max_depth=6, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=3, n_estimators=1000, total= 11.5s [CV] max_depth=6, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=3, n_estimators=1000, total= 11.6s [CV] max_depth=6, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=6, min_child_weight=3, n_estimators=1000, total= 11.5s [CV] max_depth=9, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=100, total= 3.3s [CV] max_depth=9, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=100, total= 3.3s [CV] max_depth=9, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=100, total= 3.3s [CV] max_depth=9, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=100, total= 3.3s [CV] max_depth=9, min_child_weight=1, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=100, total= 3.3s [CV] max_depth=9, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=200, total= 4.6s [CV] max_depth=9, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=200, total= 4.6s [CV] max_depth=9, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=200, total= 4.6s [CV] max_depth=9, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=200, total= 4.6s [CV] max_depth=9, min_child_weight=1, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=200, total= 4.6s [CV] max_depth=9, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=300, total= 5.7s [CV] max_depth=9, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=300, total= 5.7s [CV] max_depth=9, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=300, total= 5.7s [CV] max_depth=9, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=300, total= 5.8s [CV] max_depth=9, min_child_weight=1, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=300, total= 5.7s [CV] max_depth=9, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=400, total= 6.7s [CV] max_depth=9, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=400, total= 6.7s [CV] max_depth=9, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=400, total= 6.7s [CV] max_depth=9, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=400, total= 6.8s [CV] max_depth=9, min_child_weight=1, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=400, total= 6.7s [CV] max_depth=9, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=500, total= 7.7s [CV] max_depth=9, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=500, total= 7.7s [CV] max_depth=9, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=500, total= 7.7s [CV] max_depth=9, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=500, total= 7.8s [CV] max_depth=9, min_child_weight=1, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=500, total= 7.7s [CV] max_depth=9, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=600, total= 8.7s [CV] max_depth=9, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=600, total= 8.7s [CV] max_depth=9, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=600, total= 8.7s [CV] max_depth=9, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=600, total= 8.7s [CV] max_depth=9, min_child_weight=1, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=600, total= 8.7s [CV] max_depth=9, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=700, total= 9.6s [CV] max_depth=9, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=700, total= 9.6s [CV] max_depth=9, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=700, total= 9.6s [CV] max_depth=9, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=700, total= 9.7s [CV] max_depth=9, min_child_weight=1, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=700, total= 9.6s [CV] max_depth=9, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=800, total= 10.6s [CV] max_depth=9, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=800, total= 10.6s [CV] max_depth=9, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=800, total= 10.6s [CV] max_depth=9, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=800, total= 10.7s [CV] max_depth=9, min_child_weight=1, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=800, total= 10.6s [CV] max_depth=9, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=900, total= 11.5s [CV] max_depth=9, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=900, total= 11.6s [CV] max_depth=9, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=900, total= 11.6s [CV] max_depth=9, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=900, total= 11.6s [CV] max_depth=9, min_child_weight=1, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=1, n_estimators=900, total= 11.6s [CV] max_depth=9, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=1, n_estimators=1000, total= 12.5s [CV] max_depth=9, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=1, n_estimators=1000, total= 12.6s [CV] max_depth=9, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=1, n_estimators=1000, total= 12.5s [CV] max_depth=9, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=1, n_estimators=1000, total= 12.6s [CV] max_depth=9, min_child_weight=1, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=1, n_estimators=1000, total= 12.5s [CV] max_depth=9, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=100, total= 3.0s [CV] max_depth=9, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=100, total= 3.0s [CV] max_depth=9, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=100, total= 3.0s [CV] max_depth=9, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=100, total= 3.0s [CV] max_depth=9, min_child_weight=2, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=100, total= 3.0s [CV] max_depth=9, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=200, total= 4.2s [CV] max_depth=9, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=200, total= 4.2s [CV] max_depth=9, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=200, total= 4.2s [CV] max_depth=9, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=200, total= 4.2s [CV] max_depth=9, min_child_weight=2, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=200, total= 4.2s [CV] max_depth=9, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=300, total= 5.2s [CV] max_depth=9, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=300, total= 5.2s [CV] max_depth=9, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=300, total= 5.2s [CV] max_depth=9, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=300, total= 5.3s [CV] max_depth=9, min_child_weight=2, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=300, total= 5.2s [CV] max_depth=9, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=400, total= 6.2s [CV] max_depth=9, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=400, total= 6.2s [CV] max_depth=9, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=400, total= 6.2s [CV] max_depth=9, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=400, total= 6.3s [CV] max_depth=9, min_child_weight=2, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=400, total= 6.2s [CV] max_depth=9, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=500, total= 7.2s [CV] max_depth=9, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=500, total= 7.2s [CV] max_depth=9, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=500, total= 7.2s [CV] max_depth=9, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=500, total= 7.2s [CV] max_depth=9, min_child_weight=2, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=500, total= 7.2s [CV] max_depth=9, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=600, total= 8.1s [CV] max_depth=9, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=600, total= 8.2s [CV] max_depth=9, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=600, total= 8.1s [CV] max_depth=9, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=600, total= 8.2s [CV] max_depth=9, min_child_weight=2, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=600, total= 8.2s [CV] max_depth=9, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=700, total= 9.1s [CV] max_depth=9, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=700, total= 9.1s [CV] max_depth=9, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=700, total= 9.1s [CV] max_depth=9, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=700, total= 9.2s [CV] max_depth=9, min_child_weight=2, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=700, total= 9.1s [CV] max_depth=9, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=800, total= 10.1s [CV] max_depth=9, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=800, total= 10.1s [CV] max_depth=9, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=800, total= 10.1s [CV] max_depth=9, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=800, total= 10.1s [CV] max_depth=9, min_child_weight=2, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=800, total= 10.1s [CV] max_depth=9, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=900, total= 11.0s [CV] max_depth=9, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=900, total= 11.0s [CV] max_depth=9, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=900, total= 11.0s [CV] max_depth=9, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=900, total= 11.1s [CV] max_depth=9, min_child_weight=2, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=2, n_estimators=900, total= 11.0s [CV] max_depth=9, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=2, n_estimators=1000, total= 12.0s [CV] max_depth=9, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=2, n_estimators=1000, total= 12.0s [CV] max_depth=9, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=2, n_estimators=1000, total= 12.0s [CV] max_depth=9, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=2, n_estimators=1000, total= 12.1s [CV] max_depth=9, min_child_weight=2, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=2, n_estimators=1000, total= 12.0s [CV] max_depth=9, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=100, total= 2.8s [CV] max_depth=9, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=100, total= 2.8s [CV] max_depth=9, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=100, total= 2.8s [CV] max_depth=9, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=100, total= 2.9s [CV] max_depth=9, min_child_weight=3, n_estimators=100 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=100, total= 2.8s [CV] max_depth=9, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=200, total= 4.0s [CV] max_depth=9, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=200, total= 4.0s [CV] max_depth=9, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=200, total= 4.0s [CV] max_depth=9, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=200, total= 4.1s [CV] max_depth=9, min_child_weight=3, n_estimators=200 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=200, total= 4.0s [CV] max_depth=9, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=300, total= 5.0s [CV] max_depth=9, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=300, total= 5.0s [CV] max_depth=9, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=300, total= 5.0s [CV] max_depth=9, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=300, total= 5.1s [CV] max_depth=9, min_child_weight=3, n_estimators=300 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=300, total= 5.0s [CV] max_depth=9, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=400, total= 6.0s [CV] max_depth=9, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=400, total= 6.0s [CV] max_depth=9, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=400, total= 6.0s [CV] max_depth=9, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=400, total= 6.0s [CV] max_depth=9, min_child_weight=3, n_estimators=400 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=400, total= 6.0s [CV] max_depth=9, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=500, total= 6.9s [CV] max_depth=9, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=500, total= 6.9s [CV] max_depth=9, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=500, total= 6.9s [CV] max_depth=9, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=500, total= 7.0s [CV] max_depth=9, min_child_weight=3, n_estimators=500 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=500, total= 7.0s [CV] max_depth=9, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=600, total= 7.9s [CV] max_depth=9, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=600, total= 7.9s [CV] max_depth=9, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=600, total= 7.9s [CV] max_depth=9, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=600, total= 8.0s [CV] max_depth=9, min_child_weight=3, n_estimators=600 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=600, total= 7.9s [CV] max_depth=9, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=700, total= 8.9s [CV] max_depth=9, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=700, total= 8.9s [CV] max_depth=9, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=700, total= 8.9s [CV] max_depth=9, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=700, total= 8.9s [CV] max_depth=9, min_child_weight=3, n_estimators=700 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=700, total= 8.9s [CV] max_depth=9, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=800, total= 9.8s [CV] max_depth=9, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=800, total= 9.8s [CV] max_depth=9, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=800, total= 9.8s [CV] max_depth=9, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=800, total= 9.9s [CV] max_depth=9, min_child_weight=3, n_estimators=800 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=800, total= 9.9s [CV] max_depth=9, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=900, total= 10.8s [CV] max_depth=9, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=900, total= 10.8s [CV] max_depth=9, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=900, total= 10.8s [CV] max_depth=9, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=900, total= 10.9s [CV] max_depth=9, min_child_weight=3, n_estimators=900 ............... [CV] max_depth=9, min_child_weight=3, n_estimators=900, total= 10.8s [CV] max_depth=9, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=3, n_estimators=1000, total= 11.7s [CV] max_depth=9, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=3, n_estimators=1000, total= 11.8s [CV] max_depth=9, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=3, n_estimators=1000, total= 11.7s [CV] max_depth=9, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=3, n_estimators=1000, total= 11.8s [CV] max_depth=9, min_child_weight=3, n_estimators=1000 .............. [CV] max_depth=9, min_child_weight=3, n_estimators=1000, total= 11.8s
[Parallel(n_jobs=1)]: Done 450 out of 450 | elapsed: 54.5min finished
Best: 0.994559 using {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 600}
0.987894 (0.002699) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100}
0.992927 (0.001697) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 200}
0.993879 (0.000746) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 300}
0.994015 (0.000902) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 400}
0.994423 (0.000998) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 500}
0.994559 (0.000744) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 600}
0.994423 (0.000998) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 700}
0.994423 (0.000998) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 800}
0.994423 (0.000998) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 900}
0.994559 (0.000744) with: {'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 1000}
0.988846 (0.003293) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 100}
0.992927 (0.001999) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 200}
0.993471 (0.001645) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 300}
0.993471 (0.001753) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 400}
0.993743 (0.001632) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 500}
0.993743 (0.001632) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 600}
0.993743 (0.001632) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 700}
0.994151 (0.001400) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 800}
0.994151 (0.001400) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 900}
0.994287 (0.001400) with: {'max_depth': 3, 'min_child_weight': 2, 'n_estimators': 1000}
0.988031 (0.002903) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 100}
0.992111 (0.002043) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 200}
0.993199 (0.001773) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 300}
0.993471 (0.001642) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 400}
0.993607 (0.001951) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 500}
0.993743 (0.001844) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 600}
0.994015 (0.001687) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 700}
0.993743 (0.001844) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 800}
0.993743 (0.001844) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 900}
0.993743 (0.001844) with: {'max_depth': 3, 'min_child_weight': 3, 'n_estimators': 1000}
0.989391 (0.001525) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100}
0.991431 (0.001185) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 200}
0.992247 (0.001104) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 300}
0.992383 (0.001168) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 400}
0.992383 (0.000901) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 500}
0.992383 (0.000901) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 600}
0.992519 (0.000961) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 700}
0.992519 (0.000961) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 800}
0.992519 (0.000961) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 900}
0.992519 (0.000961) with: {'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 1000}
0.990071 (0.001464) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 100}
0.991703 (0.001688) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 200}
0.992111 (0.001527) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 300}
0.992383 (0.001688) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 400}
0.992383 (0.001688) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 500}
0.992383 (0.001688) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 600}
0.992655 (0.001688) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 700}
0.992791 (0.001527) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 800}
0.992791 (0.001527) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 900}
0.992791 (0.001261) with: {'max_depth': 6, 'min_child_weight': 2, 'n_estimators': 1000}
0.989663 (0.001316) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 100}
0.991567 (0.001752) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 200}
0.991703 (0.001451) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 300}
0.992111 (0.001104) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 400}
0.992111 (0.001104) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 500}
0.992111 (0.001104) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 600}
0.992111 (0.001104) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 700}
0.992111 (0.001104) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 800}
0.992383 (0.000792) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 900}
0.992383 (0.000792) with: {'max_depth': 6, 'min_child_weight': 3, 'n_estimators': 1000}
0.987351 (0.001698) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 100}
0.988983 (0.002485) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 200}
0.989663 (0.002630) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 300}
0.989799 (0.002471) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 400}
0.989799 (0.002471) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 500}
0.989663 (0.002292) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 600}
0.989799 (0.002150) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 700}
0.989799 (0.002150) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 800}
0.989935 (0.002292) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 900}
0.989935 (0.002292) with: {'max_depth': 9, 'min_child_weight': 1, 'n_estimators': 1000}
0.987623 (0.002166) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 100}
0.989935 (0.002485) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 200}
0.990615 (0.002167) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 300}
0.990615 (0.002167) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 400}
0.990479 (0.002150) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 500}
0.990479 (0.002150) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 600}
0.990479 (0.002316) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 700}
0.990615 (0.002371) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 800}
0.990615 (0.002371) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 900}
0.990751 (0.002493) with: {'max_depth': 9, 'min_child_weight': 2, 'n_estimators': 1000}
0.988166 (0.002134) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 100}
0.990207 (0.001855) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 200}
0.990615 (0.002292) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 300}
0.990615 (0.002292) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 400}
0.991023 (0.001989) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 500}
0.991159 (0.002017) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 600}
0.991295 (0.001990) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 700}
0.991431 (0.001999) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 800}
0.991295 (0.001990) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 900}
0.991703 (0.001742) with: {'max_depth': 9, 'min_child_weight': 3, 'n_estimators': 1000}
Model training time: 0:54:38.900996
fig = plt.figure(figsize=(16,12))
fig.suptitle('Algorithm Comparison - Post Tuning')
ax = fig.add_subplot(111)
plt.boxplot(tune_results)
ax.set_xticklabels(tune_model_names)
plt.show()
test_model = XGBClassifier(n_estimators=600, max_depth=3, min_child_weight=1, random_state=seedNum, objective='multi:softmax', num_class=6, tree_method='gpu_hist')
test_model.fit(X_train, y_train)
predictions = test_model.predict(X_test)
print('Accuracy Score:', accuracy_score(y_test, predictions))
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
print(test_model)
Accuracy Score: 0.9494401085850017
[[491 4 1 0 0 0]
[ 28 437 6 0 0 0]
[ 7 17 396 0 0 0]
[ 0 2 0 429 60 0]
[ 0 0 0 24 508 0]
[ 0 0 0 0 0 537]]
precision recall f1-score support
1 0.93 0.99 0.96 496
2 0.95 0.93 0.94 471
3 0.98 0.94 0.96 420
4 0.95 0.87 0.91 491
5 0.89 0.95 0.92 532
6 1.00 1.00 1.00 537
accuracy 0.95 2947
macro avg 0.95 0.95 0.95 2947
weighted avg 0.95 0.95 0.95 2947
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=600, n_jobs=1,
nthread=None, num_class=6, objective='multi:softprob',
random_state=888, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
seed=None, silent=None, subsample=1, tree_method='gpu_hist',
verbosity=1)
# Combining the training and testing datasets to form the complete dataset that will be used for training the final model
# X_complete = np.vstack((X_train, X_test))
# y_complete = np.concatenate((y_train, y_test))
# print("X_complete.shape: {} y_complete.shape: {}".format(X_complete.shape, y_complete.shape))
# final_model = test_model1.fit(X_complete, y_complete)
# print(final_model)
# modelName = 'FinalModel_BinaryClass.sav'
# dump(final_model, modelName)
print ('Total time for the script:',(datetime.now() - startTimeScript))
Total time for the script: 0:57:08.492687